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Citation: Ramos-Campoy, O.;
Comas-Albertí, A.; Hervás, D.;
Borrego-Écija, S.; Bosch, B.; Sandoval,
J.; Fort-Aznar, L.; Moreno-Izco, F.;
Fernández-Villullas, G.;
Molina-Porcel, L.; et al. Genome-Wide
DNA Methylation in
Early-Onset-Dementia Patients Brain
Tissue and Lymphoblastoid Cell Lines.
Int. J. Mol. Sci. 2024,25, 5445.
https://doi.org/10.3390/
ijms25105445
Academic Editor: Anna Atlante
Received: 23 April 2024
Revised: 8 May 2024
Accepted: 13 May 2024
Published: 16 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
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4.0/).
International Journal of
Molecular Sciences
Article
Genome-Wide DNA Methylation in Early-Onset-Dementia
Patients Brain Tissue and Lymphoblastoid Cell Lines
Oscar Ramos-Campoy 1, Aina Comas-Albertí1, David Hervás2, Sergi Borrego-Écija 1, Beatriz Bosch 1,
Juan Sandoval 3, Laura Fort-Aznar 1, Fermín Moreno-Izco 4,5 , Guadalupe Fernández-Villullas 1,
Laura Molina-Porcel 1,6 , Mircea Balasa 1, Albert Lladó1, Raquel Sánchez-Valle 1,7 ,*, † and Anna Antonell 1, 7,*,†
1Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona,
FRCB-IDIBAPS, Universitat de Barcelona (UB), 08036 Barcelona, Spain; acomasa@recerca.clinic.cat (A.C.-A.);
borrego@clinic.cat (S.B.-É.); bbosch@recerca.clinic.cat (B.B.); fort@recerca.clinic.cat (L.F.-A.);
gfernanv@clinic.cat (G.F.-V.); lmolinap@clinic.cat (L.M.-P.); mbalasa@clinic.cat (M.B.); allado@clinic.cat (A.L.)
2Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València,
46022 Valencia, Spain; daherma@eio.upv.es
3Epigenomics Core Facility, Health Research Institute La Fe, 46026 Valencia, Spain; epigenomica@iislafe.es
4Cognitive Disorders Unit, Department of Neurology, Hospital Universitario Donostia,
20014 San Sebastian, Spain; fermin.morenoizco@osakidetza.eus
5
Instituto de Investigación Sanitaria Biogipuzkoa, Neurosciences Area, Group of Neurodegenerative Diseases,
20014 San Sebastian, Spain
6Neurological Tissue Bank, Biobank-Hospital Clinic-IDIBAPS, 08036 Barcelona, Spain
7Facultat de Medicina i Ciències de la Salut, Institut de Neurociències, Universitat de Barcelona (UB),
08036 Barcelona, Spain
*Correspondence: rsanchez@clinic.cat (R.S.-V.); antonell@recerca.clinic.cat (A.A.)
†These authors contributed equally to this work.
Abstract: Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases,
has been in the scope of several studies performed so far. However, there is a gap in regard to
analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs).
We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal
cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer’s disease
(AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted
p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A
correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort
was also performed. The results showed hypermethylation as the most frequent finding in both
tissues studied in the patient groups. Biological significance analysis revealed common pathways
altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction,
and immune system pathways. These alterations were also found in LCL samples, suggesting the
epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation
presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive
correlation. This study enhances our understanding of the biological pathways that are associated
with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential
cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.
Keywords: Alzheimer’s disease; frontotemporal dementia; lymphoblastoid cell lines; brain tissue;
DNA methylation; diagnostic signature; epigenetic assessment
1. Introduction
Currently, more than 55 million people have dementia worldwide, and every year,
there are nearly 10 million new cases. Alzheimer’s disease (AD), the most frequent type
of neurodegenerative dementia [1], can be classified into early-onset and late-onset forms.
In early-onset AD (EOAD) (5% of AD cases), symptoms appear, according to consensus,
Int. J. Mol. Sci. 2024,25, 5445. https://doi.org/10.3390/ijms25105445 https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2024,25, 5445 2 of 19
before 65 years old [
2
]. According to the etiology, the vast majority of EOAD cases have a
sporadic origin (sEOAD) [3], but there is also a small percentage of cases (<1%) that show
an autosomal dominant pattern of inheritance (ADAD), which is caused by mutations in
three genes involved in the β-amyloid cascade: APP,PSEN1, and PSEN2 [4].
Frontotemporal dementia (FTD) refers to a group of neurodegenerative disorders
comprising different clinical syndromes ranging from behavioral to motor forms [
5
]. The
neuropathology of FTD is also heterogeneous, with diverse protein aggregates, among
which the most frequent are tau, TDP43, and FET-related proteins. One-third of patients
have an autosomal dominant pattern of inheritance (gFTD), with C9orf72,GRN, and MAPT
being the most frequent genes involved [6].
Epigenetic mechanisms, potential underlying pathogenic mechanisms of neurode-
generative diseases, have been within the scope of several studies performed so far. The
most-studied epigenetic modification is DNA methylation at cytosines in high-density
cytosine–guanine sequences (CpGs), referred to as CpG islands, carried out by DNA
methyltransferases [
7
]. Most studies investigating DNA methylation status in AD have
been performed on brain tissue and late-onset forms of AD, reporting differences in methy-
lation, mainly hypermethylation concerning controls, dysregulating the expression of genes
involved in myelination, synaptic transmission, or immune response [
8
–
11
]. Nonethe-
less, there is growing evidence that methylation is also deregulated in peripheral blood
in patients with neurodegenerative diseases, and this possibility is attracting increasing
attention [
12
,
13
]. In regard to FTD, several studies have explored the effects of methylation
changes on brain or blood in some of the gFTD-causative genes, describing that promoter
hypermethylation in GRN and C9orf72 genes leads to a decrease in gene expression [
14
–
19
].
A few works have compared more than one type of dementia to evaluate similarities
and differences regarding physiopathological mechanisms related to neurodegenerative
diseases [20–23].
Although the brain is the target organ for studying neurodegenerative diseases, lym-
phoblastoid cell lines (LCLs; B lymphocytes immortalized with Epstein–Barr virus) have
been used as an alternative model to brain tissue to study several aspects of neurodegen-
erative diseases [
24
–
27
]. They are much easier to obtain, can be acquired at earlier stages
of disease, and represent a cell culture model. To the best of our knowledge, no study has
examined and compared global gene methylation in diverse neurodegenerative diseases
in LCLs until now. However, some publications have analyzed methylome changes in pe-
ripheral blood mononuclear cells (PBMCs), finding greater global DNA methylation in AD
patients compared to healthy controls [
28
]. Moreover, regarding FTD, several studies using
LCLs have focused on surveying epigenetic changes in genes causative of this disease’s
genetic forms, mainly C9orf72 and GRN, revealing an inverse correlation between their
promoter methylation levels and gene expression [15,29].
This study aimed to analyze and compare the genome-wide methylation profiles of
sporadic and genetic forms of early-onset AD and FTD patients in two types of samples:
frozen prefrontal cortex tissue and LCLs. Moreover, using the results obtained from our
previous study [
30
], we performed a correlation analysis between methylation and gene
expression data from the same cohort.
2. Results
The principal component analysis (PCA) showed clear separation between the brain
and LCL samples. Therefore, separate analyses were conducted for each sample. No
outliers were detected after data normalization and PCA analysis.
All comparisons revealed several DMPs mainly located in CpG islands, progressively
decreasing in adjacent regions. Based on the relative position to the gene location, the
gene body was the area with more DMPs, followed by the transcription start site 1500
(TSS1500) (Figure 1). The total number of DMPs are included in Supplemental Material
S3. Overall, our statistical analyses found more DMPs in LCL comparisons of patients
Int. J. Mol. Sci. 2024,25, 5445 3 of 19
compared to controls than in the same comparisons in brain tissue, except for the GRN vs.
CTRL comparison.
Int. J. Mol. Sci. 2024, 25, 5445 3 of 19
our statistical analyses found more DMPs in LCL comparisons of patients compared to
controls than in the same comparisons in brain tissue, except for the GRN vs. CTRL com-
parison.
Figure 1. Distribution of the differentially methylated positions (DMPs) found in brain (a,b) and
LCL (c,d) comparisons. Bar plot depicting the number of brain DMPs found in each region related
to gene (a,c) and CpG islands (b,d). CpGs sites were found in different gene regions: transcription
start site (TSS1500 and TSS200), untranslated region (5′UTR and 3′UTR), first exon, and body gene.
If we take CpGs islands as reference, CpGs may be found in shores (2 kb from islands) or shelves (5
kb from islands) and may be closer to 5′ end (N) or 3′ end (S). Filters applied: adjusted-p value < 0.05.
Abbreviations: CTRL, healthy controls; sEOAD, sporadic early-onset Alzheimer’s disease; PSEN1,
autosomal dominant Alzheimer’s disease caused by mutation in PSEN1 gene; MAPT, GRN, and
C9orf72, familial frontotemporal dementia caused by mutation in MAPT, GRN, or C9orf72 genes;
sFTD-Tau, sporadic frontotemporal dementia with tau deposits; sFTD-TDP43, sporadic frontotem-
poral dementia with TDP43 deposits; LCLs, lymphoblastoid cell lines.
We also looked for epigenetic changes in genes causative of dementia, comparing
mutation carriers for the gene with controls (Supplemental Material S4). There was only
one CpG with an absolute Beta difference value > 0.2; it was located in the promoter region
of the MAPT gene (cg24801230) and hypermethylated in patients from the MAPT group.
2.1. Alzheimer’s Disease
The Beta regression model after filtering for adjusted-p value and Beta difference re-
trieved the DMPs shown in Figure 2. In all AD-patients-versus-healthy controls compari-
sons performed on both tissues, there were more hypermethylated DMPs than hypometh-
ylated DMPs.
Figure 1. Distribution of the differentially methylated positions (DMPs) found in brain (a,b) and
LCL (c,d) comparisons. Bar plot depicting the number of brain DMPs found in each region related
to gene (a,c) and CpG islands (b,d). CpGs sites were found in different gene regions: transcription
start site (TSS1500 and TSS200), untranslated region (5
′
UTR and 3
′
UTR), first exon, and body gene.
If we take CpGs islands as reference, CpGs may be found in shores (2 kb from islands) or shelves
(5 kb from islands) and may be closer to 5
′
end (N) or 3
′
end (S). Filters applied: adjusted-pvalue
< 0.05. Abbreviations: CTRL, healthy controls; sEOAD, sporadic early-onset Alzheimer’s disease;
PSEN1, autosomal dominant Alzheimer’s disease caused by mutation in PSEN1 gene; MAPT, GRN,
and C9orf72, familial frontotemporal dementia caused by mutation in MAPT,GRN, or C9orf72
genes; sFTD-Tau, sporadic frontotemporal dementia with tau deposits; sFTD-TDP43, sporadic
frontotemporal dementia with TDP43 deposits; LCLs, lymphoblastoid cell lines.
We also looked for epigenetic changes in genes causative of dementia, comparing
mutation carriers for the gene with controls (Supplemental Material S4). There was only
one CpG with an absolute Beta difference value > 0.2; it was located in the promoter region
of the MAPT gene (cg24801230) and hypermethylated in patients from the MAPT group.
2.1. Alzheimer’s Disease
The Beta regression model after filtering for adjusted-pvalue and Beta difference
retrieved the DMPs shown in Figure 2. In all AD-patients-versus-healthy controls compar-
isons performed on both tissues, there were more hypermethylated DMPs than hypomethy-
lated DMPs.
Int. J. Mol. Sci. 2024,25, 5445 4 of 19
Int. J. Mol. Sci. 2024, 25, 5445 4 of 19
Figure 2. Number of differentially methylated CpGs (DMPs). Bar plot showing the quantities of
hyper- and hypomethylated CpGs found in each comparison performed per tissue. Filters applied:
in the brain, adjusted-p value < 0.05 and absolute value of Beta difference > 0.25; in LCLs, adjusted-
p value < 0.01 and absolute value of Beta difference > 0.35. Abbreviations: CTRL, healthy controls;
sEOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant Alzheimer’s disease
caused by mutation in PSEN1 gene; MAPT, GRN, and C9orf72, familial frontotemporal dementia
caused by mutation in MAPT, GRN, or C9orf72 genes; sFTD-Tau, sporadic frontotemporal dementia
with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia with TDP43 deposits; LCLs,
lymphoblastoid cell lines.
We then selected the 10 DMPs with the highest Beta difference values for each com-
parison regarding the brain and LCL samples (Figure 3 and Supplemental Material S5).
Figure 3. Heatmaps with the top 10 differentially methylated positions (DMPs) in each AD compar-
ison performed. For each CpG, the associated gene is given in parentheses. (a) Brain sEOAD vs.
CTRL; (b) brain PSEN1 vs. CTRL; (c) LCL sEOAD vs. CTRL; (d) LCL PSEN1 vs. CTRL. Abbrevia-
tions: CTRL, healthy controls; EOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal
dominant Alzheimer’s disease caused by mutation in PSEN1 gene; LCLs, lymphoblastoid cell lines.
Figure 2. Number of differentially methylated CpGs (DMPs). Bar plot showing the quantities of
hyper- and hypomethylated CpGs found in each comparison performed per tissue. Filters applied:
in the brain, adjusted-pvalue < 0.05 and absolute value of Beta difference > 0.25; in LCLs, adjusted-p
value < 0.01 and absolute value of Beta difference > 0.35. Abbreviations: CTRL, healthy controls;
sEOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant Alzheimer ’s disease
caused by mutation in PSEN1 gene; MAPT, GRN, and C9orf72, familial frontotemporal dementia
caused by mutation in MAPT, GRN, or C9orf72 genes; sFTD-Tau, sporadic frontotemporal dementia
with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia with TDP43 deposits; LCLs,
lymphoblastoid cell lines.
We then selected the 10 DMPs with the highest Beta difference values for each com-
parison regarding the brain and LCL samples (Figure 3and Supplemental Material S5).
Int. J. Mol. Sci. 2024, 25, 5445 4 of 19
Figure 2. Number of differentially methylated CpGs (DMPs). Bar plot showing the quantities of
hyper- and hypomethylated CpGs found in each comparison performed per tissue. Filters applied:
in the brain, adjusted-p value < 0.05 and absolute value of Beta difference > 0.25; in LCLs, adjusted-
p value < 0.01 and absolute value of Beta difference > 0.35. Abbreviations: CTRL, healthy controls;
sEOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant Alzheimer’s disease
caused by mutation in PSEN1 gene; MAPT, GRN, and C9orf72, familial frontotemporal dementia
caused by mutation in MAPT, GRN, or C9orf72 genes; sFTD-Tau, sporadic frontotemporal dementia
with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia with TDP43 deposits; LCLs,
lymphoblastoid cell lines.
We then selected the 10 DMPs with the highest Beta difference values for each com-
parison regarding the brain and LCL samples (Figure 3 and Supplemental Material S5).
Figure 3. Heatmaps with the top 10 differentially methylated positions (DMPs) in each AD compar-
ison performed. For each CpG, the associated gene is given in parentheses. (a) Brain sEOAD vs.
CTRL; (b) brain PSEN1 vs. CTRL; (c) LCL sEOAD vs. CTRL; (d) LCL PSEN1 vs. CTRL. Abbrevia-
tions: CTRL, healthy controls; EOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal
dominant Alzheimer’s disease caused by mutation in PSEN1 gene; LCLs, lymphoblastoid cell lines.
Figure 3. Heatmaps with the top 10 differentially methylated positions (DMPs) in each AD compari-
son performed. For each CpG, the associated gene is given in parentheses. (a) Brain sEOAD vs. CTRL;
(b) brain PSEN1 vs. CTRL; (c) LCL sEOAD vs. CTRL; (d) LCL PSEN1 vs. CTRL. Abbreviations: CTRL,
healthy controls; EOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant
Alzheimer’s disease caused by mutation in PSEN1 gene; LCLs, lymphoblastoid cell lines.
Int. J. Mol. Sci. 2024,25, 5445 5 of 19
2.1.1. Common DMPs in AD Subtypes
We found nine common DMPs in the brain in the AD patients (genetic and sporadic)
versus control comparisons. The DMPs were found in the genes BMPR1B,TAGLN3,GCH1,
DHX37,FAM65C,ABCA1,ACCN1,EPHB3, and GLIS1. In the patients, all of these genes
were hypermethylated except for TAGLN3, which was hypomethylated. On the other hand,
in LCLs, there were only two DMPs in common in the same group comparisons (located in
the genes ANXA6 and CATSPER2), both of which were hypermethylated in the patients
(Figure 4).
Int. J. Mol. Sci. 2024, 25, 5445 5 of 19
2.1.1. Common DMPs in AD Subtypes
We found nine common DMPs in the brain in the AD patients (genetic and sporadic)
versus control comparisons. The DMPs were found in the genes BMPR1B, TAGLN3,
GCH1, DHX37, FAM 65C, ABCA1, ACCN1, EPHB3, and GLIS1. In the patients, all of these
genes were hypermethylated except for TAGLN3, which was hypomethylated. On the
other hand, in LCLs, there were only two DMPs in common in the same group compari-
sons (located in the genes ANXA6 and CATSPER2), both of which were hypermethylated
in the patients (Figure 4).
Figure 4. Common differentially methylated CpGs (DMPs) found in different AD comparisons.
Venn diagrams showing common DMPs in brain (a) and LCL comparisons (b). Underneath each
Venn diagram, a table shows the common DMPs found and their corresponding gene, Beta differ-
ence (Beta dif.), and adjusted-p value (Adj.pval). Filters applied: in brain, adjusted-p value < 0.05
and absolute value of Beta difference > 0.25; in LCLs, adjusted-p value < 0.01 and absolute value of
Beta difference > 0.35. Abbreviations: CTRL, healthy controls; sEOAD, sporadic early-onset Alz-
heimer’s disease; PSEN1, autosomal dominant Alzheimer’s disease caused by mutation in PSEN1
gene; LCLs, lymphoblastoid cell lines.
2.1.2. Biological Significance Analysis
Analysis of biological significance found several significant pathways (from Reac-
tome) and biological processes (BPs; from GO-BP) altered in all AD comparisons. Neural
system pathways were found in brain tissue, including with respect to BPs such as axon
development (PSEN1 vs. CTRL). In LCLs, neurotransmitter receptors and long-term poten-
tiation were obtained in sEOAD vs. CTRL comparisons by applying Reactome database.
Metabolic disturbances were also a common outcome in all the comparisons per-
formed, affecting carbohydrate metabolism (all brain comparisons) or phosphorylation
regulation (all LCL comparisons). Immune system pathways were present, in both tissues,
affecting cytokine signaling and adaptive response. Finally, pathways related to the
MECP2 gene expression node and its regulation of neural cell terms were found in both
groups of patients in LCLs.. The complete list of BPs and pathways can be found in Sup-
plemental Materials S6 and S7.
Figure 4. Common differentially methylated CpGs (DMPs) found in different AD comparisons. Venn
diagrams showing common DMPs in brain (a) and LCL comparisons (b). Underneath each Venn
diagram, a table shows the common DMPs found and their corresponding gene, Beta difference
(Beta dif.), and adjusted-pvalue (Adj.pval). Filters applied: in brain, adjusted-pvalue < 0.05 and
absolute value of Beta difference > 0.25; in LCLs, adjusted-pvalue < 0.01 and absolute value of Beta
difference > 0.35. Abbreviations: CTRL, healthy controls; sEOAD, sporadic early-onset Alzheimer’s
disease; PSEN1, autosomal dominant Alzheimer’s disease caused by mutation in PSEN1 gene; LCLs,
lymphoblastoid cell lines.
2.1.2. Biological Significance Analysis
Analysis of biological significance found several significant pathways (from Reactome)
and biological processes (BPs; from GO-BP) altered in all AD comparisons. Neural system
pathways were found in brain tissue, including with respect to BPs such as axon develop-
ment (PSEN1 vs. CTRL). In LCLs, neurotransmitter receptors and long-term potentiation
were obtained in sEOAD vs. CTRL comparisons by applying Reactome database.
Metabolic disturbances were also a common outcome in all the comparisons per-
formed, affecting carbohydrate metabolism (all brain comparisons) or phosphorylation
regulation (all LCL comparisons). Immune system pathways were present, in both tissues,
affecting cytokine signaling and adaptive response. Finally, pathways related to the MECP2
gene expression node and its regulation of neural cell terms were found in both groups of
patients in LCLs. The complete list of BPs and pathways can be found in Supplemental
Materials S6 and S7.
Int. J. Mol. Sci. 2024,25, 5445 6 of 19
2.2. Frontotemporal Dementia
The Beta regression model results were filtered by adjusted-pvalue and Beta difference
variables and a shorter list of DMPs was obtained, shown in Figure 2. Hypermethylation
was the most frequent alteration in the brain samples from the patients, in comparison
withhealthy controls.
We also selected the 10 DMPs with the highest Beta difference for each comparison in
the brain and LCLs samples (Figure 5and Supplemental Materials S5 and S8).
Int. J. Mol. Sci. 2024, 25, 5445 6 of 19
2.2. Frontotemporal Dementia
The Beta regression model results were filtered by adjusted-p value and Beta differ-
ence variables and a shorter list of DMPs was obtained, shown in Figure 2. Hypermeth-
ylation was the most frequent alteration in the brain samples from the patients, in com-
parison withhealthy controls.
We also selected the 10 DMPs with the highest Beta difference for each comparison
in the brain and LCLs samples (Figure 5 and Supplemental Materials S5 and S8).
Figure 5. Heatmaps with the top 10 differentially methylated positions (DMPs) in some of the FTD
comparisons performed. Each CpG obtained has its associated gene beside it. (a) Brain MAPT vs.
CTRL; (b) brain GRN vs. CTRL; (c) brain sFTD-TDP43 vs. sFTD-Tau; (d) LCLs MAPT vs. CTRL; (e)
LCLs GRN vs. CTRL. Abbreviations: CTRL, healthy controls; MAPT and GRN, familial frontotem-
poral dementia caused by mutation in MAPT or GRN genes; sFTD-Tau, sporadic frontotemporal
dementia with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia with TDP43 deposits;
LCLs, lymphoblastoid cell lines.
2.2.1. Common DMPs in FTD Subtypes
We found four common DMPs between all the FTD-patient-subtype-versus-healthy
controls comparisons involving brain tissue. These DMPs were located in the genes
BMPR1B, SRPK2, BTBD8, and MYBPC1, all of which were hypermethylated in the patients
(Figure 6). By comparing the three genetic patient groups with the controls, we found 12
shared DMPs. On the other hand, comparing the two sporadic patient groups with the
controls showed 42 DMPs in common. These DMPs were mostly hypermethylated in the
patients (39/42), and there was a complete concordance about the hyper- or hypomethyl-
ation status both in sFTD with tau deposits and sFTD with TDP43 deposits (Supplemental
Material S9).
Figure 5. Heatmaps with the top 10 differentially methylated positions (DMPs) in some of the FTD
comparisons performed. Each CpG obtained has its associated gene beside it. (a) Brain MAPT vs.
CTRL; (b) brain GRN vs. CTRL; (c) brain sFTD-TDP43 vs. sFTD-Tau; (d) LCLs MAPT vs. CTRL;
(e) LCLs GRN vs. CTRL. Abbreviations: CTRL, healthy controls; MAPT and GRN, familial frontotem-
poral dementia caused by mutation in MAPT or GRN genes; sFTD-Tau, sporadic frontotemporal
dementia with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia with TDP43 deposits;
LCLs, lymphoblastoid cell lines.
2.2.1. Common DMPs in FTD Subtypes
We found four common DMPs between all the FTD-patient-subtype-versus-healthy
controls comparisons involving brain tissue. These DMPs were located in the genes BMPR1B,
SRPK2,BTBD8, and MYBPC1, all of which were hypermethylated in the patients (Figure 6).
By comparing the three genetic patient groups with the controls, we found 12 shared DMPs.
On the other hand, comparing the two sporadic patient groups with the controls showed
42 DMPs in common. These DMPs were mostly hypermethylated in the patients (39/42), and
there was a complete concordance about the hyper- or hypomethylation status both in sFTD
with tau deposits and sFTD with TDP43 deposits (Supplemental Material S9).
Int. J. Mol. Sci. 2024,25, 5445 7 of 19
Int. J. Mol. Sci. 2024, 25, 5445 7 of 19
Figure 6. Common differentially methylated CpGs (DMPs) found between different comparisons in
FTD. Venn diagrams showing common DMPs in brain (a) and LCL comparisons (b). The table un-
derneath the Venn diagram (a) shows the common DMPs found and their correspondent genes, Beta
differences (Beta dif.), and adjusted-p values (Adj.pval). Filters applied: in brain, adjusted-p value is
< 0.05 and absolute value of Beta difference is >0.25; in LCLs, adjusted-p value is <0.01 and absolute
value of Beta difference is >0.35. Abbreviations: CTRL, healthy controls; MAPT, GRN, and C9orf72,
familial frontotemporal dementia caused by mutation in MAPT, GRN, or C9orf72 genes; sFTD-Tau,
sporadic frontotemporal dementia with tau deposits; sFTD-TDP43, sporadic frontotemporal de-
mentia with TDP43 deposits; LCLs, lymphoblastoid cell lines.
2.2.2. Biological Significance Analysis
Biological significance analysis of the FTD groups comparisons also identified signif-
icant GO-BPs and pathways from the Reactome resource, except for the brain C9orf72 vs.
CTRL comparison, which did not show any significant GO-BPs. Nervous system BPs were
frequently found in all sporadic and genetic patients versus healthy controls comparisons
regarding brain tissue, as well as in the LCL comparison of the MAPT vs. CTRL groups.
They were specifically related to neuron development and differentiation.
Deregulated immune system pathways were found in the sporadic and genetic FTD
groups with TDP43 deposition in both tissues. Signal transduction and protein metabo-
lism pathways were found in the sporadic and genetic groups with tau deposition. The
complete lists of the BPs and pathways found are included in Supplemental Materials S6
and S7.
2.3. Common Differentially Methylated CpGs in AD and FTD
Just one common DMP was found in all the patients-versus-controls comparisons for
brain tissue, namely, cg17925226, which is located in the promoter region of the BMPR1B
gene and was hypermethylated in all patientsgroups. No common DMPs were obtained
in any of the patients-versus-control comparisons regarding LCLs. The list of common
DMPs obtained can be found in Supplemental Material S10.
2.4. Diagnostic Signatures
We applied an Elastic Net model to obtain a list of CpGs that allowed differentiation
of the groups for each pairwise comparison of patients versus controls. These lists of CpGs
have a diagnostic signature potential when analyzed all together in the groups of interest
(Supplemental Material S11).
Figure 6. Common differentially methylated CpGs (DMPs) found between different comparisons
in FTD. Venn diagrams showing common DMPs in brain (a) and LCL comparisons (b). The table
underneath the Venn diagram (a) shows the common DMPs found and their correspondent genes,
Beta differences (Beta dif.), and adjusted-pvalues (Adj.pval). Filters applied: in brain, adjusted-pvalue
is < 0.05 and absolute value of Beta difference is >0.25; in LCLs, adjusted-pvalue is <0.01 and absolute
value of Beta difference is >0.35. Abbreviations: CTRL, healthy controls; MAPT, GRN, and C9orf72,
familial frontotemporal dementia caused by mutation in MAPT,GRN, or C9orf72 genes; sFTD-Tau,
sporadic frontotemporal dementia with tau deposits; sFTD-TDP43, sporadic frontotemporal dementia
with TDP43 deposits; LCLs, lymphoblastoid cell lines.
2.2.2. Biological Significance Analysis
Biological significance analysis of the FTD groups comparisons also identified signifi-
cant GO-BPs and pathways from the Reactome resource, except for the brain C9orf72 vs.
CTRL comparison, which did not show any significant GO-BPs. Nervous system BPs were
frequently found in all sporadic and genetic patients versus healthy controls comparisons
regarding brain tissue, as well as in the LCL comparison of the MAPT vs. CTRL groups.
They were specifically related to neuron development and differentiation.
Deregulated immune system pathways were found in the sporadic and genetic FTD
groups with TDP43 deposition in both tissues. Signal transduction and protein metabolism
pathways were found in the sporadic and genetic groups with tau deposition. The complete
lists of the BPs and pathways found are included in Supplemental Materials S6 and S7.
2.3. Common Differentially Methylated CpGs in AD and FTD
Just one common DMP was found in all the patients-versus-controls comparisons for
brain tissue, namely, cg17925226, which is located in the promoter region of the BMPR1B
gene and was hypermethylated in all patientsgroups. No common DMPs were obtained in
any of the patients-versus-control comparisons regarding LCLs. The list of common DMPs
obtained can be found in Supplemental Material S10.
2.4. Diagnostic Signatures
We applied an Elastic Net model to obtain a list of CpGs that allowed differentiation
of the groups for each pairwise comparison of patients versus controls. These lists of CpGs
have a diagnostic signature potential when analyzed all together in the groups of interest
(Supplemental Material S11).
Int. J. Mol. Sci. 2024,25, 5445 8 of 19
2.5. Pyrosequencing Validation
The DMPs selected for validation were located in the TUBAL3 and ABCA1 genes.
After performing non-parametric statistical analysis (using the Mann–Whitney U test for
TUBAL3 and the Kruskal–Wallis test for ABCA1), both of them were validated, as shown
in Supplemental Material S2. The DMP in TUBAL3 was significant when comparing both
groups of sporadic FTD, while the DMP in ABCA1 was significant in the sporadic and
genetic AD brain groups compared to healthy controls.
2.6. Correlation Analysis
A correlation analysis between gene expression and DNA methylation microarrays
data from the same individuals was performed, considering the DMPs for each comparison.
Significant results (adjusted-pvalue < 0.05, absolute value of correlation coefficient > 0.7
and absolute value of Beta difference > 0.1) are shown in Table 1.
Table 1. Correlation between CpGs obtained in the methylation analysis and genes found in the
expression array.
BRAIN
Comparison CpG Gene Correlation
Relation to Nearest Gene Relation to CpG Island
PSEN1 vs. sEOAD cg16550453 TDRD1 −0.8336 TSS200 Island
sFTD-Tau vs. CTRL cg12150421 KIF17 −0.7075 Body S_Shore
sFTD-TDP43 vs.
CTRL cg12150421 KIF17 −0.7075 Body S_Shore
GRN vs. CTRL cg24203376 TDRD1 −0.8060 TSS200 N_Shore
cg05726248 TESPA1 −0.7070 TSS1500; ExonBnd; Body 0
LCLs
sEOAD vs. CTRL
cg17369694 HLA.DRB5 0.8304 3′UTR 0
cg01341801 HLA.DRB5 0.8436 Body N_Shore
cg22730830 PRSS21 −0.8250 Body Island
cg01232511 PRSS21 −0.8564 Body Island
PSEN1 vs. CTRL cg09074040 ANKDD1A 0.7978 Body 0
PSEN1 vs. sEOAD cg21817187 SARM1 0.7820 Body N_Shore
MAPT vs. CTRL
cg17369694 HLA.DRB5 0.8304 3′UTR 0
cg01341801 HLA.DRB5 0.8436 Body N_Shore
cg05072008 FIGNL1 0.7839 TSS1500 Island
cg22730830 PRSS21 −0.8250 Body Island
cg01232511 PRSS21 −0.8564 Body 0
cg25206919 TRIM72 0.8269 Body; 3′UTR Island
GRN vs. CTRL cg17369694 HLA.DRB5 0.8304 3′UTR 0
cg01341801 HLA.DRB5 0.8436 Body N_Shore
GRN vs. MAPT cg05072008 FIGNL1 0.7839 TSS1500 Island
cg20322685 BAIAP2L1 0.8185 Body 0
Comparisons which do not appear did not have any significative result. For each CpG, it is also shown its relation
to the nearest gene and its relative position with respect to a CpG island. Filters applied: adjusted-pvalue < 0.05;
absolute value of Beta difference Beta difference > [0.1]; correlation coefficient (r) > [0.7]. Abbreviations: CTRL,
healthy controls; sEOAD, sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant Alzheimer’s dis-
ease due to mutation in PSEN1; MAPT, GRN, familial frontotemporal dementia due to mutation in MAPT or GRN;
sFTD-Tau, sporadic frontotemporal dementia with accumulation of tau; sFTD-TDP43, sporadic frontotemporal
dementia with accumulation of TDP43; LCLs, lymphoblastoid cell lines.
In brain tissue, all DMPs exhibited a negative correlation between DNA methylation
levels and gene expression. We found two CpGs in the GRN vs. CTRL comparison
(cg24203376 within the TDRD1 gene and cg05726248 within the TESPA1 gene) and one CpG
Int. J. Mol. Sci. 2024,25, 5445 9 of 19
common to both comparisons of sporadic FTD patients versus healthy controls (cg12150421
within the KIF17 gene).
Unlike in brain tissue, in LCLs, the majority of DMPs had a positive correlation
between DNA methylation and gene expression. We detected two CpGs (cg17369694
and cg01341801) correlating with HLA-DRB5 expression in three comparisons: sEOAD
vs. CTRL, MAPT vs. CTRL, and GRN vs. CTRL. Moreover, two CpGs (cg22730830 and
cg01232511) correlated with PRSS21 gene expression, found in sEOAD vs. CTRL and
MAPT vs. CTRL, which had a negative correlation. Other significant results are presented
in Table 1.
3. Discussion
Here, we present the results of a genome-wide DNA methylation study conducted on
groups of patients with familial and sporadic forms of early-onset AD and FTD using two
types of samples: brain prefrontal cortex tissue and LCLs.
Previous evidence regarding methylation alterations in brain samples from AD pa-
tients has exhibited some degree of variability. Several studies, mainly centered on late-
onset AD, reported an increased level of methylation in the frontal cortex [
31
–
33
], an
observation that is consistent with our findings. This hypermethylation does not seem
region-dependent since other brain areas, like the hippocampus or entorhinal cortex, also
exhibited hypermethylation [
31
,
34
]. However, there are also reports indicating that hy-
pomethylation was more commonly observed [
9
,
35
]. The variability in the results could be
attributed to the varying cellular compositions of the studied samples, as there is evidence
suggesting differences in the degree of methylation among different types of nervous
system cells [
36
]. Another potential contributing factor is the dynamic nature of methy-
lation, as epigenetic mechanisms are not static and may undergo changes influenced by
environmental factors or aging [37].
Few researchers have used lymphoblastoid cell lines (LCLs) from AD patients to
investigate DNA methylation changes. Instead, several studies have employed peripheral
blood mononuclear cells (PBMCs) or whole blood, with some of them comparing DNA
methylation patterns between brain tissue and whole blood [13,38–40].
Our findings obtained from FTD patients also revealed hypermethylation to be the
most common observation in brain samples. To the best of our knowledge, there are no
previous reports analyzing the whole-genome methylation statuses of FTD patients, either
in brain tissue or in LCLs. Previous studies using brain samples have specifically investi-
gated the methylation statuses of genes implicated in FTD, particularly C9orf72 and GRN.
Other studies have employed whole-blood samples obtained from sporadic FTD patients,
but a consensus regarding the predominance of hyper- or hypomethylation compared to
controls has not been reached. Some studies have reported increased methylation in FTD
patients compared to controls [
41
], while others have found hypomethylation to be more
prevalent in patients [42].
3.1. Alzheimer’s Disease
We searched for the top DMPs with the highest Beta difference values, obtained in
each of the comparisons of patients versus controls. In brain tissue, among these CpGs, it is
important to highlight that some of them are located in genes involved in immune response
function. One of these DMPs, found in the brain PSEN1 vs. CTRL comparison, is a CpG
related to the HLA-DRB1 gene. It is a member of the major histocompatibility complex
and plays an important role in immune response regulation. Prior studies have defined it
as a susceptibility gene for developing AD, particularly in early-onset forms [
43
,
44
]. The
top methylated DMPs in LCLs also showed interesting results, since many DMPs were
related to genes that are somehow associated with AD. In the sEOAD vs. CTRL comparison,
we found the SGK1 gene, which seems to act as a survival factor and whose expression
has been reported to be increased in AD patients [
45
]. A DMP associated with DYSF,
Int. J. Mol. Sci. 2024,25, 5445 10 of 19
another gene reported to be overexpressed in AD patients [
46
], was found in the PSEN1 vs.
CTRL comparison.
Regarding common DMPs in sporadic and genetic patients, we found nine DMPs in
common in brain tissue and two DMPs in common in LCLs. Some of the common DMPs
found in the brain are within genes known to confer a risk for developing AD, like GLIS1, a
regulator of transcription, or ABCA1, a membrane transporter [
47
,
48
]. Interestingly, another
DMP was related to the GCH1 gene, which encodes a key enzyme in dopamine synthesis
and whose variants have been reported to be a risk factor for Parkinson’s disease [
49
].
In LCLs, one of the two DMPs in common is located in the ANXA6 gene, which belongs
to a family of genes whose proteins regulate the interface between the membrane and
cytoplasm. ANXA6 has been shown to interact with axonal tau protein, contributing to
its pathological distribution [
50
]. We also searched for common DMPs between different
tissues and in the same comparison, but we obtained few matches.
The most frequently altered biological pathways in brain tissue were related to the
metabolism of carbohydrates, steroids, or catabolic process regulation. Metabolic distur-
bances are a known feature in AD, recognized not only in gene expression studies [
51
] but
also in those that analyze methylome differences [
52
]. The fact that metabolism dysregula-
tion was found both in sporadic and genetic patients makes it a common element in the
evolution of neurodegeneration and establishes metabolism as a key factor for cellular sur-
vival. In our study, other altered pathways found in genetic patients were those associated
with neural development and neurotransmitter regulation. Nervous system development
or synaptic transmission pathways have been found in previous studies [
9
,
52
,
53
], and there
is evidence of some variable methylated regions being over-represented in these pathways,
which have been reported to correlate with AD neuropathology [54].
In LCLs, pathways related to metabolic processes were observed, and they were
similar to those found in the brain. However, there is limited evidence regarding the
biological processes associated with epigenetic changes in LCLs. The presence of these
changes in patients at an early stage of this disease may suggest that they have been altered
since the onset of the neurodegenerative process. Pathways related to MECP2 gene function
were identified in both sporadic and genetic LCLs AD patients’ comparisons. This gene
encodes for a protein capable of binding methylated DNA and acting as a transcriptional
repressor [
55
]. Although it is primarily linked to autism spectrum disorders, studies on
animal models have indicated a potential association between MECP2 alterations and AD
pathogenesis, as well as cognitive decline [
56
–
59
]. Moreover, recent research on humans
suggests that MECP2 may unveil a novel etiopathogenetic mechanism of sporadic AD [
60
].
3.2. Frontotemporal Dementia
We examined the top DMPs in the comparisons of sporadic FTD patients, which were
ranked by Beta difference values. None of the top ten DMPs identified in the sporadic FTD
group overlapped with the top 10 DMPs found in the AD groups. However, we identified
numerous genes previously implicated in other neurodegenerative diseases, although not
specifically in FTD. This suggests that there are potential shared molecular mechanisms
among these diseases. One of these genes was CUL3, found in the sFTD-Tau vs. CTRLs
comparison, a gene with multiple functions in cell cycle regulation, synaptic control, and
proteasomal degradation that has recently been found to be downregulated in AD animal
models [
61
,
62
]. The DNAJB6 gene showed a significant difference between individuals with
sFTD-TDP43 and CTRLs. This gene has been reported to be dysregulated in Parkinson’s
disease and multiple system atrophy [
63
], and there is a reported case of FTD caused by its
mutation [
64
]. Upon comparing both types of sporadic FTD patients, we found a DMP in
the SOX5 gene, a gene involved in corticospinal motor neuron development and a known
risk factor for developing amyotrophic lateral sclerosis (ALS) [
65
]. As ALS is part of the ALS-
FTD clinical spectrum and shares the most common cause of disease with FTD (G4C2-repeat
expansion in C9orf72), this finding could be attributed to common underlying mechanisms.
Int. J. Mol. Sci. 2024,25, 5445 11 of 19
Additionally, anotherDMP within the TUBAL3 gene was identified when comparing both
types of sporadic FTD patients, and this finding was validated using pyrosequencing.
Regarding the genetic FTD group comparisons, some of the top ten DMPs were shared
with the top DMPs identified in the sporadic and familial AD patients compared to the
controls. An example is the differentially methylated position (DMP) associated with the
HLA-DRB1 gene, which was identified in the C9orf72 group. Previous reports have indicated
that this gene is downregulated in FTD [
66
]. Our data indicate the hypermethylation
of a DMP located within the gene body, which likely results in decreased expression.
Similarly, PTPRN2, which encodes a transmembrane protein involved in neurotransmitter
secretion, was found to be hypermethylated in the GRN group. There is evidence suggesting
decreased expression of PTPRN2 in both AD and FTD [
67
]. Lastly, in the C9orf72 group, we
observed altered methylation of the TP73 gene, variants of which have been linked to ALS
and FTD [68,69].
In LCLs from genetic FTD patients, we also identified the top DMPs in common with
AD, which were associated with the LGAL8,ANXA6, or HLA-DRB1 genes. Other DMPs
found were within the genes HLA-DRB5 or YWHAG, which have also been identified as
risk factors in the development of FTD and Parkinson’s disease, respectively [70,71].
To the best of our knowledge, only a few reports have studied altered pathways in
FTD using genome-wide methylome analysis. In this cohort, we observed that neuronal
development and differentiation pathways were commonly disrupted in FTD brains and
were present in the majority of the patient-versus-control comparisons, a result that is
consistent with findings from other studies [
41
]. Immune pathways were dysregulated
in the GRN and sFTD-TDP43 groups compared to the controls, particularly those related
to cytokine release and the adaptive immune system. These results suggest a potentially
significant role of inflammation in FTD characterized by TDP43 deposition. Nonetheless, in
patients with tau deposition, we found metabolic dysregulation and numerous pathways
related to altered signaling and ERBB family genes, which encode for tyrosine kinase
receptors and have been identified as risk factors for developing ALS or FTD [72].
In LCLs, we were able to analyze the MAPT and GRN groups, and we found altered
neural pathways and cytokine signaling pathways, both of which were also found in the
FTD brain tissue results. Finally, like in the LCLs in AD comparisons, we found many
dysregulated pathways related to the MECP2 gene, which may point towards altered
neuron maturation and toxicity in FTD as well [56].
3.3. Correlation Analysis
The correlation analysis between the gene expression array data and methylome array
data from the same cohort showed interesting correlations between differential CpGs
methylation status and gene expression. We found a negative correlation between the
methylation grade of cg12150421 and the expression of the KIF17 gene in brain tissue, a
correlation observed in both groups of sporadic FTD patients. KIF17 is responsible for
transporting cargo along microtubules. In LCLs, we found more CpGs that exhibited a
significant correlation with expression levels than in brain tissue. Some of these CpGs were
shared between AD and FTD patients (sEOAD, MAPT, and GRN groups versus healthye
controls). For instance, we identified two DMPs (cg17369694 and cg01341801) associated
with the same gene, HLA-DRB5. Throughout all the mentioned comparisons, we observed
that both CpGs were hypomethylated and displayed a positive correlation, suggesting that
HLA-DRB5 expression levels are lower in LCLs of these groups of patients.
One limitation of our study is the relatively limited number of subjects in each sub-
group, which could have constrained the statistical power of the analysis. Nonetheless,
we have obtained results with significant adjusted-pvalues. Another limitation is the un-
availability of LCLs from sporadic FTD and C9orf72 groups, which precludes comparisons
in both tissues. Regarding the use of LCLs, despite the immortalized nature of this cell
culture model, the main aim of this study was to compare data obtained from this biological
sample with brain tissue data and evaluate the former as a plausible biological model for
Int. J. Mol. Sci. 2024,25, 5445 12 of 19
the study of neurodegenerative diseases. Lastly, our study focused on DNA methyla-
tion, an epigenetic mechanism that can modulate gene expression at a pre-transcriptional
level. However, other epigenetic modifications, such as chromatin modifications and
changes in regulatory RNAs (miRNA), are also relevant for the development of AD. For
instance, microRNAs (miRNAs) regulate gene expression at the post-transcriptional level,
thereby repressing mRNA translation, and they have been extensively studied in relation
to AD [
73
]. Moreover, histone modifications such as lysine acetylation or arginine/lysine
methylation represent post-translational epigenetic modifications that activate or repress
gene transcription [
74
,
75
]. So, considering these factors collectively is essential for ob-
taining a comprehensive understanding of chromatin compaction and how it regulates
gene transcription.
In conclusion, our study presents data on genome-wide DNA methylation in a broad
spectrum of AD and FTD subtypes, revealing potential targets for future biomarkers or
therapeutic strategies and identifying altered biological pathways. Additionally, we pro-
vide evidence supporting the usefulness of LCLs in neurodegenerative diseases research.
As future research steps, we would like to determine if differential methylation is translated
to differential gene expression for some of the most interesting genes regarding neurode-
generative diseases and to validate some differential methylation findings obtained from
whole blood, specifically those found in LCLs.
4. Materials and Methods
4.1. Samples and Clinical Data
We selected 64 samples from two different tissues: frozen prefrontal cortex (n= 40)
and LCLs (n= 24). Frozen prefrontal cortex tissue was obtained from the Neurological
Tissue Bank of IDIBAPS-Hospital Clínic de Barcelona (NTB-IHC) (n= 38) and Basque
Biobank-Biodonostia (n= 2). We included brain samples obtained from healthy controls
(CTRL; n= 5), sEOAD (n= 5) and patients with ADAD due to mutations in PSEN1 gene
(PSEN1; n= 5), genetic FTD caused by mutations in MAPT,GRN or C9orf72 genes (MAPT
n= 5; GRN n= 5; C9orf72 n= 5), and sporadic FTD with tau deposits (sFTD-Tau; n= 5) and
TDP43 deposits (sFTD-TDP43; n= 5). All brain donors had died in advanced clinical stages
after several years of disease duration.
LCLs were obtained from subjects who visited the Alzheimer’s disease and other cog-
nitive disorders Units of Hospital Clínic de Barcelona, Barcelona, Spain (n= 24). The
quantities of samples included from each group are as follow: CTRL n= 5; sEOAD
n= 5; PSEN1 n= 6; MAPT n= 3; and GRN n= 5. C9orf72 mutated samples and spo-
radic FTD samples were not available. All AD patients (both sEOAD and PSEN1 groups)
had their diseases biologically confirmed using cerebrospinal fluid biomarkers and pre-
sented a typical amnestic phenotype. To stage dementia severity, we applied the Clinical
Dementia Rating scale—Global Score (CDR-GS). In sEOAD group, 2 out of 5 patients had a
CDR-GS of 0.5, while the other 3 had a CDR-GS of 1. In the PSEN1 group, the majority of
subjects had a CDR-GS of 1 (4/6); one had a CDR-GS of 0.5, and the last one had a CDR-GS
of 2. Genetic FTD patients developed behavioral variant FTD. Only one MAPT mutation
carrier was diagnosed with semantic dementia, which eventually led to behavioral variant
FTD. Regarding dementia staging, in the MAPT group, 2 out of 3 patients had a CDR-GS of
0.5, and the rest had a CDR-GS of 3. Finally, in the GRN group, 3 out of 5 subjects had a
CDR-GS of 2, and the remaining two had a CDR-GS of 3.
Demographics and other relevant variables are shown in Table 2. Considering all
pairwise comparisons performed for each tissue, no significant differences in any variable
were found. More information about patients harboring genetic mutations is shown in
Supplemental Material S1.
Int. J. Mol. Sci. 2024,25, 5445 13 of 19
Table 2. Demographics of each group included.
Samples
(n= 64)
Sex;
Female/Male
(% Female)
Age at
Sampling
Age at
Onset
Years since
Onset
Post
Mortem
Delay (h)
Passage
Number
BRAIN
(n= 40)
CTRL 5 2/3 (40) 54.0 ±19.3 // // 11.2 ±6.5 //
sEOAD 5 3/2 (60) 67.0 ±4.4 55.6 ±4.4 11.5 ±5.6 7.3 ±2.6 //
PSEN1 5 3/2 (60) 54.0 ±3.7 41.8 ±7.2 12.2 ±7.8 9.6 ±5.7 //
MAPT 5 1/4 (20) 60.2 ±7.4 52.8 ±4.7 7.4 ±3.8 11.4 ±3.5 //
GRN 5 3/2 (60) 69.6 ±5.2 62.2 ±6.7 7.4 ±2.1 15.6 ±4.4 //
C9orf72 5 2/3 (40) 71.4 ±14.8 60.0 ±14.9 9.3 ±1.7 7.4 ±3.3 //
sFTD-Tau 5 1/4 (20) 70.0 ±6.6 60.4 ±5.9 9.6 ±3.6 8.3 ±5.7 //
sFTD-
TDP43 5 2/3 (40) 71.2 ±9.4 57.4 ±7.1 13.8 ±3.6 11.8 ±1.8 //
LCLs
(n= 24)
CTRL 5 2/3 (40) 48.8 ±10.3 // // // 6.6 ±0.5
sEOAD 5 2/3 (40) 59.8 ±4.7 55.4 ±3.2 4.4 ±1.8 // 5.6 ±1.5
PSEN1 6 2/4 (33) 49.3 ±6.6 44.8 ±7.4 4.5 ±2.0 // 5.2 ±0.8
MAPT 3 2/1 (67) 67.0 ±11.7 59.7 ±9.5 7.3 ±7.6 // 6.0 ±2.0
GRN 5 5/0 (100) 59.0 ±3.3 56.0 ±2.5 3.0 ±1.3 // 5.8 ±2.0
Mean values (
±
SD) are represented for each subject group. Abbreviations: CTRL, healthy controls; sEOAD,
sporadic early-onset Alzheimer’s disease; PSEN1, autosomal dominant Alzheimer’s disease due to mutation
in PSEN1; MAPT, GRN, C9orf72, familial frontotemporal dementia due to mutation in MAPT,GRN or C9orf72;
sFTD-Tau, sporadic frontotemporal dementia with accumulation of tau; sFTD-TDP43, sporadic frontotemporal
dementia with accumulation of TDP43; LCLs, lymphoblastoid cell lines.
This study was performed in line with the principles of the Declaration of Helsinki.
All patients had signed informed consent forms for sample donation. This project was
approved by the local Ethics Committee of the Hospital Clínic de Barcelona.
4.2. Lymphoblastoid Cell Line Isolation and Culturing
Cell lines were grown in suspension in upright T-25 flasks (Sarstedt, Nümbrecht,
Germany). They were maintained with RPMI-1640 GlutaMAX medium supplemented
with 10% inactivated FBS and 1% penicillin/streptomycin (Gibco, Thermofisher, Waltham,
MA, USA). Each flask contained 12 mL of medium and was kept in a humidified incubator
at 37
◦
C with 5% CO
2
. Cell lines had a mean passage number between 6–7 when obtaining
the pellet (10 ×106cells) for DNA extraction.
4.3. DNA Extraction and Array Processing
DNA was extracted from 20 mg of frozen prefrontal cortex samples using QIAmp
DNA Mini Kit (Qiagen, Hilden, Germany), with a DNA yield of 56–188 ng/
µ
L in a final
volume of 80
µ
L. DNA extraction from LCLs (10
×
10
6
cells) was performed with AllPrep
DNA/RNA/Protein Mini Kit (Qiagen), following the manufacturer’s protocol, with a
DNA yield of 60–930 ng/
µ
L in a final volume of 50
µ
L. Before performing the methylation
studies, DNA integrity quality control was performed, and DNA samples were treated
with RNaseA for 1 h at 45 ◦C.
A total of 600 ng of purified DNA was randomly distributed on a 96-well plate and
processed using an EZ-96 DNA Methylation kit (Zymo Research Corp., Irvine, CA, USA),
following the manufacturer’s recommendations for Infinium assays. Bisulfite-converted
DNA was hybridized using an Infinium DNA MethylationEPIC BeadChip (Illumina Inc.,
San Diego, CA, USA). The efficiency of bisulfite conversion reaction was checked, with
Illumina’s internal controls being optimal in all cases and without significant differences
between sample groups.
Int. J. Mol. Sci. 2024,25, 5445 14 of 19
Microarray data are available in the ArrayExpress database (http://www.ebi.ac.uk/
arrayexpress, accessed on 1 January 2024) under accession number E-MTAB-11975.
4.4. Microarray Data and Biological Significance Analysis
Data analysis was performed using the statistical language R (https://www.r-project.
org/, accessed on 1 January 2024) (version 4.1.0) and the following packages: Rfit (version
0.24.2), betareg (version 3.1-4), glmnet (version 4.1-1), IlluminaHumanMethylationEPIC-
manifest (version 0.3.0), NMF (version 0.23.0), Rtsne (version 0.15), pcaMethods (version
1.79.1), VennDiagram (version 1.6.20), and UpsetR (version 1.4.0).
After functional normalization, CpGs with a pvalue > 0.01 and those related to
sexual chromosomes were filtered. A Beta regression model, with age and sex included
as covariates, was applied to obtain differentially methylated positions (DMPs), and all
p-values were adjusted according to the False Discovery Rate (FDR). Filters applied for
further analyses of common DMPs between comparisons were: adjusted-pvalue < 0.05
and an absolute value of Beta difference > 0.25 for brain samples, and adjusted-pvalue
< 0.01 and an absolute value of Beta difference > 0.35 in LCLs data; these filters were
applied to obtain a manageable and similar number of DMPs in each comparison’s list. In
addition to the Beta regression model, we performed an Elastic Net logistic regression [
76
].
This is a penalized linear regression model that allows the identification of a list of CpGs
that may classify subjects to a diagnostic group. Sex and age were also included in the
analyses as covariates. Selection of the regularization parameter was performed using
10-fold cross-validation with 200 repeats.
Venn diagrams (http://www.interactivenn.net/index.html, accessed on 1 January
2024) [
77
] and pseudo-Venn figures (UpsetR package in R) were created to visualize com-
mon genes between pairwise comparisons. Heatmaps were also produced, showing the
top 10 DMPs with the highest Beta difference absolute value.
The biological significance analysis of the significative DMPs was performed by using
Reactome Pathway Knowledge database (https://reactome.org/, accessed on 1 January
2024) and Gene Ontology (GO) resource with ShinyGO 0.76 tool (http://bioinformatics.
sdstate.edu/go/, accessed on 1 January 2024) [
78
,
79
] in an over-representation analysis,
using the following filters for Beta regression data: brain tissue, adjusted-pvalue < 0.05,
and absolute value of Beta difference > 0.23, and for LCLs, adjusted-pvalue < 0.01 and
absolute value of Beta difference > 0.32.
4.5. Pyrosequencing
Briefly, genomic DNA was bisulfite-converted as described above; then, polymerase
chain reaction (PCR) was performed using standard conditions with biotinylated primers
(primer sequences are shown in Supplemental Material S2). Pyrosequencing reactions
and methylation quantification were performed using PyroMark Q24 System version 2.0.7
(Qiagen), using appropriate reagents and recommended protocols.
Statistical analysis was conducted with GraphPad Prism (version 8.0.2) using non-
parametric tests.
4.6. Correlation between DNA Methylation Data and Gene Expression Array Data
We performed a correlation analysis of the Beta regression and Elastic Net results of
each comparison with the expression level of all the genes from Clariom D array data from
our previous study [
20
] using Pearson correlation coefficient (r), taking into account the
closest 5 kb up- and downstream of each studied gene. Results show each tissue analyzed
independently and using the following filters: an absolute value of r> 0.7, adjusted-pvalue
< 0.05, and an absolute value of Beta difference > 0.1.
Supplementary Materials: The following supporting information can be downloaded at https:
//www.mdpi.com/article/10.3390/ijms25105445/s1.
Int. J. Mol. Sci. 2024,25, 5445 15 of 19
Author Contributions: Conceptualization, R.S.-V. and A.A.; methodology, O.R.-C., L.F.-A., A.C.-A.
and G.F.-V.; software, D.H. and J.S.; validation, O.R.-C.; formal analysis, O.R.-C., D.H. and A.A.;
resources, B.B., S.B.-É., F.M.-I., L.M.-P., M.B. and A.L.; data curation, O.R.-C. and L.M-P.; writing—
original draft preparation, O.R.-C.; writing—review and editing, R.S.-V. and A.A.; supervision, R.S.-V.
and A.A.; project administration, A.A.; funding acquisition, R.S.-V. and A.A. All authors have read
and agreed to the published version of the manuscript.
Funding: This study has been funded by Instituto de Salud Carlos III (ISCIII) through the projects
“PI17/00670” to A.A., “PI20/00448” to R.S.-V. and “FI18/00121” to O.R.-C., and co-funded by
the European Union. Departament de Salut de la Generalitat de Catalunya (PERIS 2016–2020,
“SLT002/16/00329” and PERIS 2019–2021, “SLT008/18/00061”); Departament de Recerca i Universi-
tats de la Generalitat de Catalunya, AGAUR group 2021-SGR-01126. The Article Processing Charge
will be funded by University of Barcelona.
Institutional Review Board Statement: The study was conducted in accordance with the Declara-
tion of Helsinki and approved by the local Ethics Committee of the Hospital Clínic de Barcelona
(HCB/2017/0802, approved in 11 December 2017) and the Ethics committee of Biobanc-Hospital
Clinic-FRCB-IDIBAPS and Basque Biobank.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Written informed consent has been obtained from the patients to publish this paper.
Data Availability Statement: Microarray data are available in the ArrayExpress database (http:
//www.ebi.ac.uk/arrayexpress, accessed on 1 January 2024) under accession number E-MTAB-11975.
Acknowledgments: The authors thank the patients and their relatives for their participation in
research. We are also indebted to the Biobanc-Hospital Clinic-FRCB-IDIBAPS for their aid regarding
the samples and data procurement, as well as to Diana Garcia from the Epigenomics core facility of
Hospital La Fe of Valencia for her technical support with the EPIC arrays.
Conflicts of Interest: The authors declare no conflicts of interest. R.S.-V. reports personal fees from
Wave pharmaceuticals for attending Advisory board meetings; personal fees from Roche diagnostics,
Janssen, and Neuraxpharm for educational activities; and research grants to her institution from
Biogen and Sage Therapeutics outside the submitted work.
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