Abstract
Epigenetic modulations lead to changes in gene expression, including
DNA methylation, histone modifications, and noncoding RNAs. In recent
years, epigenetic modifications have been related to the pathogenesis
of different types of cancer, cardiovascular disease, and other
diseases. Emerging evidence indicates that DNA methylation could be
associated with ischemic stroke (IS) and plays a role in pathological
progression, but the underlying mechanism has not yet been fully
understood. In this study, we used human methylation 850K BeadChip to
analyze the differences in gene methylation status in the peripheral
blood samples from two groups (3 IS patients vs. 3 healthy controls).
According to their bioinformatics profiling, we found 278 genes with
significantly different methylation levels. Seven genes with the most
significant methylation modifications were validated in two expanded
groups (100 IS patients vs. 100 healthy controls). The CAMTA1 gene had
significantly different methylation changes in patients compared to the
controls. To understand the CAMTA1 function in stroke, we generated
CAMTA1 knockout in SH-SY5Y cells. RNA seq results in CAMTA1 knockout
cells revealed the pathways and gene set enrichments involved in
cellular proliferation and cell cycle. Furthermore, a series of
experiments demonstrated that in the oxygen-glucose
deprivation/re-oxygenation (OGD/R) model system, the expression of
cyclin D1, an essential regulator of cell cycle progression, was
increased in SH-SY5Y CAMTA1 KO cells. Increasing evidence demonstrated
that ischemic stress could inappropriately raise cyclin D1 levels in
mature neurons. However, the molecular signals leading to an increased
cyclin D1 level are unclear. Our findings demonstrate for the first
time that the CAMTA1 gene could regulate cyclin D1 expression and
implicate their role in strokes.
Keywords: ischemic stroke, DNA methylation modification, CAMTA1, CCND1,
cell cycle
Introduction
A stroke is an acute cerebrovascular incident. It affects the arteries
leading to and within the brain, preventing oxygen and nutrients. About
80% of strokes are caused by ischemic compared to 20% hemorrhagic. It
is one of the leading causes of death and disability worldwide.
Currently, the stroke burden remains high, with 5.5 million deaths in
2016 based on the Global Burden of Disease report (Fogh et al.,
[55]2016). Besides, the stroke burden has also increased in young
people aged 18–49 (GBD 2016 Stroke Collaborators, [56]2019). That
implies massive public health issues and needs scaled-up prevention
strategies. Therefore, a complete understanding of the pathogenesis of
ischemic stroke (IS) is required.
Although substantial evidence has pointed to numerous environmental and
genetic risk factors associated with IS (Giralt-Steinhauer et al.,
[57]2014; Feigin et al., [58]2017; GBD 2016 Stroke Collaborators,
[59]2019; Singer et al., [60]2019), additional mechanisms remain
clarified. Over the past years, with the improvement of research in the
epigenetic field, multiple studies have revealed the epigenetic
modifications involved in the pathogenesis of the cardiovascular
disease (Traylor et al., [61]2012; Singer et al., [62]2019). One of the
most understood epigenetic modifications, DNA methylation, usually
occurs on the CpG site by adding one or more methyl groups to a
cytosine. It affects gene transcription and expression without changing
DNA sequences (Morgado-Pascual et al., [63]2018). Abnormal DNA
methylation patterns have been investigated in IS pathogenesis
(Feinberg, [64]2007; Udali et al., [65]2013). The global level of DNA
methylation increased in the rat model of IS (Deng et al., [66]2019).
Recently, more studies demonstrated that gene-specific methylation
could also be involved in IS. For example, the hypermethylation in the
cystathionine-beta-synthase promoter (Stanzione et al., [67]2020) and
the AHCY gene encoding the S-adenosine homocysteine hydrolase (Dock et
al., [68]2015) have been identified to increase ischemic stroke risk.
People with hypomethylation in Long Interspersed Nucleotide Element-1
(LINE-1) may also have a higher risk of IS (Wang et al., [69]2019).
Additionally, the higher methylation of cyclin-dependent kinase
inhibitor 2B (CDKN2B) may affect arterial calcification in IS patients
(Zhao et al., [70]2020). Despite these results, the new gene-specific
methylation and its mechanisms remain unexplored.
Additionally, dysregulation of cell cycle machinery is also implicated
in strokes. Empirical evidence suggests that inappropriately activated
complex cyclin D1/cyclin-dependent kinases (Cdk) under ischemic stress
conditions were responsible for the dysregulated cell cycle. The cyclin
D1 levels were increased by the oxygen-glucose deprivation (Cai et al.,
[71]2009; Baccarelli et al., [72]2010b; Zhou et al., [73]2016). In
addition, an increase in cyclin D1 immunoreactivity has also been
detected in human stroke brains (Katchanov et al., [74]2001). However,
the molecular signals leading to an increased cyclin D1 level have not
been clarified.
In this study, we conducted a genome-wide analysis extracted from the
peripheral blood to identify differentially methylated genes in IS
patients using Illumina Infinium Methylation EPIC Bead Chip (850K chip)
and Methyl Target (target regional methylation level sequencing).
CAMTA1 gene was the most highly methylated in patients compared to the
controls. It encodes synthetic Calmodulin Binding Transcription
Activator 1 (CAMTA1), which could inhibit the proliferation of various
tumor cells, including breast cancer, colon cancer, pheochromocytoma,
etc. Current research on the CAMTA1 protein has focused on its role in
the pathogenesis of Epithelioid hemangioendothelioma (EHE), a malignant
tumor of vascular endothelial cell origin. The primary mechanism of its
pathogenesis is multiple translocations in the chromosome 1p36.3 and
3q25 regions, which happens to be the location of the CAMTA1 gene.
Long-term studies have found that WWTR1-CAMTA1 gene fusion occurs in
90% of EHE cases. Under the transcriptional control of the WWTR1
promoter, this fusion gene activates the abnormally high expression of
the CAMTA1 gene, encoding a specific fusion transcription factor that
plays a crucial role in the EHE pathogenesis. Few studies have reported
that the CAMTA1 gene was also associated with neurodegenerative
diseases. Fogh et al. have shown that the CAMTA1 gene affects the
survival of patients with sporadic amyotrophic lateral sclerosis (ALS)
(Fogh et al., [75]2016). Studies have shown that CAMTA1 KO mice could
develop ataxia, Purkinje fibrosis, and other characteristics (Long et
al., [76]2014). However, the involvement of CAMTA1 in stroke has not
been reported. Our results show that CAMTA1 knockout could attenuate
oxygen-glucose deprivation/re-oxygenation (OGD/R)-induced apoptosis and
block more cells at the S phase. Moreover, the CCND1 mRNA and its
coding protein: cyclin D1, were increased by decreased CAMTA1 levels.
Our findings demonstrate that the CAMTA1 gene affects the
ischemia-reperfusion injury by regulating cyclin D1 proteins.
Materials and methods
Study population
The study was conducted on three healthy people and three acute
cerebral infarction patients with magnetic resonance imaging scans
performed in the Department of Neurology, the First Affiliated Hospital
of the Henan University of Chinese Medicine, from May 2017 to September
2017. Two men and one woman with an average age of 57.3 years were in
the IS group. Likewise, in the healthy control group, there are two men
and one woman with an average age of 58.7 years. The validation of the
population included 100 IS patients (60 men and 40 women), with an
average age ranging between 58.34 and 72.1 years. The control group
included 100 control groups (including 52 men and 48 women), with an
average age ranging between 51.3 and 74.8 years.
Inclusion criteria: (1) The diagnosis of ischemic stroke conformed to
the guideline for the diagnosis and treatment of acute ischemic stroke
in China in 2010 and was confirmed by head MRI scans. (2) The course of
the stroke was <1 week. (3) Over 18 years of age. Exclusion criteria:
(1) Patients accompanied by unconsciousness, aphasia, or severe
cognitive impairment could not cooperate with the examination. (2)
Patients with psychosis or other psychiatric conditions such as
anxiety, depression, and suicidal behavior. (3) Patients with other
severe systemic diseases, including infection, cardiac and pulmonary
failure, or hepatic and renal dysfunction. (4) Patients failed to
perform MRI scans for various reasons. The Ethics Committee approved
the study protocol of the First Affiliated Hospital of the Henan
University of Chinese Medicine. All participants signed a written
consent form.
Expanding clinical validation of population: peripheral blood samples
were obtained from IS patients and stored at −80°C freezer. The
inclusion criteria and exclusion criteria, as detailed above, were
followed.
Extraction of proteins
For mononuclear cell pellets, 100 μL of cell lysate buffer was added to
the centrifuge tube (RIPA lysate is mixed well with PMSF in a 100:1
ratio) and readied for use. It was pipetted repeatedly until it was
mixed well and the lysate was then transferred to a new 1.5 ml EP tube.
It was incubated on ice for 30 min, during which time it was vortexed
every 10 min to allow the cells to be fully lysed. The mixture was
centrifuged at 12,000 rpm for 15 min at 4°C and the supernatant was
transferred into a new 1.5 ml EP tube to obtain the total soluble
protein from the mononuclear cells for subsequent experiments and
stored at −80°C.
DNA methylation
Peripheral blood samples from the six subjects were collected and used
for the 850K DNA methylation analysis. The DNA microarray results were
validated using the remaining samples.
After the frozen blood samples were thawed, the DNA from human whole
blood cells was extracted with a Tiangen kit (Beijing, China, Catalog
Number: DP319). DNA concentration and purity were quantified in a
NanoDrop 2000 system (NanoDrop, Wilmington, DE). The DNA concentration
must be higher than 50 ng/μl.
Whole DNA methylation (3 IS vs. 3 healthy controls) was assessed with
the Infinium Human Methylation 850 BeadChip Kit (Illumina, Inc., San
Diego, CA, United States), covering the human genome's 853,307 cytosine
positions. Data preprocessing and analyses were conducted in the
statistical programming environment R v3.1.2 with RnBeads v0.99.
Normalization and background correction was applied to the methylation
data with manufacture-recommended algorithms and implemented in the
methylome package. Methylation levels were averaged for the replicates
for each biopsy after normalization. We calculated the difference in
methylation β-value between the two groups or the mean of the pairwise
difference for paired samples. False discovery rates (FDR) were
calculated using an improved Benjamini-Hochberg procedure to correct
p-values for multiple hypothesis testing, and the methylation changes
in CpG sites/regions with FDR < 0.05 were considered statistically
significant. Ingenuity Pathway Analysis (IPA) was used to identify
functional interactions of genes differentially methylated between
groups. Average methylation signals on the CpG sites within each CpG
site and/or promoter region were hierarchically clustered with Pearson
dissimilarity and average linkage as clustering parameters.
Screening of differential genes
Difference setting criteria: The absolute value of the Diff Score value
between the case group and the control group sample was >13, and the
Absolute value of Delta Beta was >0. 17, that is, the differential
methylation gene.
Diff score = 10^*sgn(13ref-13cond)4logl0(p) For a P-value of 0.05, Diff
Score = 4–13
For a P-value of 0.01. Diff Score = 4–20
For a P-value of 0.001. Diff Score = 4–30.
The Ddta Beta value was calculated as the difference between the case
group and the control group and Avg Beta was the degree of methylation
difference between the case group and the control group at each site.
Verifying candidate genes by methyl target region methylation sequencing
The methylation level of the promoter region of the CAMTA1 gene in 100
IS cases vs. 100 healthy control samples was detected by methyl target
region methylation sequencing. MethylTarget™ assays (targeted bisulfite
sequencing) developed by Genesky Biotech (Shanghai, China) were carried
out as previously described. Briefly, CpG sites adjacent to the
promoter region of the CAMTA1 gene were analyzed, and based on these
CpG sites, four CpG regions from CpG sites in CAMTA1 were sequenced
(the relative distance from the transcriptional start site,
amplification primers, and product size of these CpG regions are
described in Tables 2, 3). Genomic DNA was converted with bisulfite,
and PCR was performed to amplify the targeted DNA sequences. The
products were sequenced by an Illumina MiSeq benchtop sequencer
(Illumina, CA, United States).
The total RNA was extracted using the Tiangen reagent (Beijing, China,
Catalog Number: DP424). Using a QuantiTect Reverse Transcription kit
(Vazyme, Wuhan, China, Catalog Number: R333-01), 2 μg of each RNA was
reverse transcribed into cDNA. Expression levels of the genes were
analyzed using a QuantiTect SYBR Green PCR kit (Vazyme, Nanjing, China,
Catalog Number: Q221-01). The primer sequences are listed below:
* Forward primer: gattatggtttgttttttaggatgagag
* Reverse primer: aacccrattcaaactcrttcc.
Western blot
Cells were lysed with RIPA lysis buffer and completed with protease
inhibitor (Solarbio, Beijing, China, Catalog Number: R0020). Lysates
were centrifuged at 4°C, 12,000 × g for 10 min, supernatants collected,
and protein concentrations assessed using a BCA protein assay kit
(Solarbio, Beijing, China, Catalog Number: PC0020). Equal amounts of
protein were placed on 10% SDS-PAGE gels and blotted onto
polyvinylidene difluoride membranes (Millipore, Hercules, CA, USA
Catalog Number: IPVH00010). Membranes were blocked with 5% nonfat milk
for 2 h at room temperature and then probed with CAMTA1(Abcam;
Cambridge, UK, Catalog Number: ab251843), and cyclin D1 (ProteinTech,
Wuhan, Hubei, China, Catalog Number: 60186-1-Ig) antibodies at 4°C
overnight. The blots were then incubated with HRP conjugated secondary
antibody. GAPDH and β-actin were used as an endogenous protein control.
ECL substrates were used to visualize signals (ProteinTech, Wuhan,
Hubei, China, Catalog Number: 60004-1-Ig).
Bioinformatic analysis
For 850K chip and RNA seq omics results, we performed GO function
annotation analysis based on the GO database
([77]http://geneontology.org/page/go-database), and KEGG pathway
annotation analysis based on the KEGG database
([78]http://www.kegg.jp/kegg/ko.html).
Cell culture and generation of CAMTA1 knockout cell lines
Human neuroblastoma cell lines (SH-SY5Y) were maintained in Dulbecco's
minimum essential medium (DMEM/F12, Seven biotech, Shanghai, China,
Catalog Number: SC103-01) with 10% fetal bovine serum (FBS, ExCell Bio,
Shanghai, China, Catalog Number: FSS500) and 1% penicillin/streptomycin
(Gbico, MA, USA, Catalog 30-2220). All cells were cultured in an
incubator with 5% CO[2] at 37°C. Lipofectamine 3000 (Invitrogen,
Shanghai, China, Catalog Number: L3000150) was used for miRNA
transfection. Cells were assayed 48 h after transfection.
Knockout cell lines were generated using the CRISPR/Cas9 system. Cells
were transfected with a px330 vector (Addgene, MA, USA, Catalog Number:
42230) encoding a gRNA for the gene of interest and a vector encoding a
gRNA for the homo sapiens CAMTA1 gene (5′-ggtatgtcgggaacctctcc-3′).
Resistant clones were expanded after adding blasticidin selection (4
μg/ml).
To generate knockout HEK293T and SH-SY5Y cells, they were transfected
with pLentiCRISPRv2 vector (Addgene, MA, USA, Catalog Number: 52961)
encoding gRNAs targeting non-overlapping regions of the CAMTA1 gene.
Following puromycin selection (2 μg/ml, for 2 days), single cell clones
were expanded, and gene disruptions were validated by sequencing.
The gRNA sequence CCGGGTCCTCCTCCGTAGTG was used to generate the SH-SY5Y
CAMTA1 knockout clone.
siRNA transfection
CCND1 siRNA and non-sense siRNA (random siRNA) were purchased from
Hanbio (Shanghai, China). The efficiency of transfection was evaluated
using real-time RT-PCR. The sequences of CCND1 and scrambled siRNAs are
as follows: Target Sequence: CCACAGATGTGAAGTTCATTT, scrambled siRNA:
CCGAAGTTACTATGAACAA. Transient transfection with siRNA was performed
using Lipofectamine RNAiMAX reagent (Invitrogen), and siRNA was reverse
transfected into cells according to the supplied protocol.
Cell counting kit-8 assay and oxygen glucose deprivation/reoxygenation model
We applied cell counting kit-8 (Topscience, Shanghai, China, Catalog
Number: TP1197) to assess cell proliferation. The cells were seeded on
the 96-well plate with a density of 1,000 cells/well, 10 μL 5 mg/mL
CCK-8 reagent was added to the well at 0, 24, 48, and 72 h. The culture
was terminated 1 h after CCK- 8 regent adding, and the optical density
OD value of each well was detected by a microplate reader (Tecan,
Mannedorf, Switzerland) at 450 nm. The experiments were repeated in
triplicate for each group.
OGD/R is the most common cell model in the study of ischemic stroke: 5
× 10^5 cells were seeded in a 35 mm culture dish, and 2 ml of complete
DMEM/F12 medium was added and placed in a regular cell incubator for a
whole night. Further, 5 × 10^5 cells were seeded in a 35 mm culture
dish, 2 ml of complete DMEM medium was added and placed in a standard
cell incubator for 4 h. Then cells were replaced with serum-free,
dual-antibiotics and low-sugar DMEM medium (DMEM, sbjbio, Nanjing,
China, Catalog Number: BC-M-038), and the cells were placed in an
anaerobic incubator (HENGZI-HYQX-II, Shanghai, China) without O[2] at
37°C for 4 h. (This step was to complete hypoglycemia and hypoxia and
we checked cell viability at the corresponding time point).
The cells were taken out and replaced with a high-sugar complete medium
and placed back into the regular cell incubator for several hours.
(Reperfusion was completed in this step and cell viability was checked
after 4 h).
Colony formation assay
The cells were seeded on the 6-well plates with a density of 10,000
cells/well in triplicate in 3 ml of medium containing 10% FBS and
allowed to grow for 3 days. The culture medium was replaced every day.
After incubation, the medium was removed. The colonies were fixed with
4% paraformaldehyde for 15 min and then stained with hematoxylin for 15
min. The stained cells were rinsed three times with tap water to remove
the excess dye. Each dish was then washed and dried. The colonies with
a diameter larger than 0.6 mm were counted.
Cell cycle assay
The cell cycle progression was assessed via a Cell Cycle Analysis kit
(Beyotime, Shanghai, China, Beyotime, Shanghai, China, Catalog Number:
C1052) in compliance with the manufacturer's instruction book. Then,
cell proportion was measured at each phase through a flow cytometer (BD
Biosciences, San Diego, CA, United States).
TUNEL assay
According to the manufacturer's protocol, apoptotic DNA fragmentation
was examined using the One Step TUNEL Apoptosis Assay kit (Beyotime
Institute of Biotechnology, Haimen, China, Catalog Number: C1089).
Briefly, cells were seeded into 24-well plates, respectively. Then,
cells were fixed in 4% paraformaldehyde for 30 min at 4°C,
permeabilized in 0.1% Triton X-100 for 2 min on ice, followed by the
TUNEL assay for 1 h at 37°C. Cy3 (Cyanine 3)-labeled TUNEL-positive
cells were imaged under a fluorescence microscope.
Luciferase reporter gene
HEK293T CAMTA1 KO cells (6 × 10^5 cells/well) were cultured in 96-well
plates and co-transfected with the control vector, CAMTA1
overexpression vector, and the Renilla plasmid using Lipofectamine 3000
(Invitrogen, USA). The concentration of CAMTA1 overexpression vector is
gradient-increasing from 0 to 100 ng. The procedure is performed
according to the protocol of the kit (Catalog Number: MA0520-1).
Firefly luciferase values were normalized to Renilla luciferase values,
and the resulting ratios were used to express luciferase activities.
Statistical analysis
We used SPSS 21.0 to perform statistical analysis. The distribution of
variables was tested with the Kolmogorov Smirnov normal distribution
test. Student's t-test or nonparametric test compared means of
designated comparison groups.
Results
Characteristics of the patients included in the microarray analysis and
validation
The population in Microarray: Total DNAs from the peripheral blood
samples of three patients and three controls were extracted for the
genomic DNA methylation assay. [79]Table 1 presents the clinical
characteristics of these six people (3 IS patients vs. 3 healthy
controls). Compared to the control group, there were significant
increases in TC, TG, and LDL levels and significant decreases in HDL
levels in the IS group (P < 0.01), and no other significant differences
were observed between these two groups.
Table 1.
Characteristics of the patients included in the microarray analysis.
Types IS Con t/χ^2-value P-value
(n = 3) (n = 3)
Gender (male/female) 2/1 2/1 - -
Age (years) 60.3 ± 9.5 65.6 ± 7.2 −0.986 0.38
Weight (kg) 62.5 ± 7.3 68.6 ± 8.0 0.976 0.385
MAP (mmHg) 95.1 ± 13.7 102.3 ± 15.2 0.609 0.575
BMI (kg/m^2) 22.1 ± 4.3 24.9 ± 3.6 4.877 0.008
Total cholesterol (mmol/L) 7.55 ± 0.64 5.21 ± 0.53 0.929 0.405
Blood sugar (mmol/L) 6.930 ± 2.58 6.383 ± 2.89 0.245 0.819
Triglyceride (mmol/L) 3.581 ± 0.791 2.128 ± 0.169 3.111 0.036
HDL (mmol/L) 0.903 ± 0.252 1.422 ± 0.194 2.827 0.048
LDL (mmol/L) 3.834 ± 0.960 2.101 ± 0.683 2.938 0.042
HbA1c (%) 6.58 ± 1.74 6.13 ± 1.95 0.298 0.780
[80]Open in a new tab
MAP, mean artery pressure; BMI, body mass index; LDL, low density
lipoprotein; HDL, high density lipoprotein.
The population of validation: Total DNA from the peripheral blood
samples of 100 patients and 100 controls was extracted for the blood
genomic DNA methylation assay. [81]Table 2 lists the clinical
characteristics of these 200 people (100 IS patients and 100 controls).
TC, TG, LDL, and HDL levels were significantly different between these
two groups. More details of these two groups can be seen in
[82]Supplementary Tables 1, [83]2.
Table 2.
Characteristics of the patients of the verification population.
Types (n = 100) (n = 100) t/χ^2-value P-value
Gender (male/female) 55/45 58/42 0.183 0.669
Age (years) 61.3 ± 8.5 67.4 ± 7.8 0.083 0.934
Weight (kg) 69.5 ± 7.6 67.6 ± 8.5 1.82 0.07
MAP (mmHg) 102.3 ± 16.6 96.3 ± 12.8 2.858 0.005
BMI (kg/m^2) 23.3 ± 4.6 22.6 ± 4.3 1.112 0.268
Total cholesterol (mmol/L) 4.73 ± 0.55 5.50 ± 1.51 4.791 0.000
Blood sugar (mmol/L) 6.59 ± 2.58 6.17 ± 2.36 1.201 0.231
Triglyceride (mmol/L) 2.881 ± 0.531 3.958 ± 0.459 15.334 0.000
Albumin (g/L) 40.63 ± 4.73 39.38 ± 5.53 1.718 0.087
HDL (mmol/L) 1.243 ± 0.252 0.979 ± 0.194 8.301 0.000
LDL (mmol/L) 2.689 ± 0.570 3.156 ± 0.669 5.313 0.000
[84]Open in a new tab
MAP, mean artery pressure; BMI, body mass index; LDL, low density
lipoprotein; HDL, high density lipoprotein.
Global changes in blood genomic methylation patterns in IS
We used Genome Studio V2018 software to report the β-values of 853,307
DNA methylation sites for the samples from the controls (n = 3) and the
IS patients (n = 3) ([85]Figure 1A). Statistical analysis revealed that
622 sites showed a difference in the degree of methylation; 502 sites
were hypermethylated, and 120 sites were hypomethylated, with a ratio
of 4:12. Manhattan map shows the distribution of methylation sites on
chromosomes ([86]Figure 1B).
Figure 1.
[87]Figure 1
[88]Open in a new tab
850K core piece difference in step base point result. (A) Heat map
showing methylation level (β-value) in cases and controls. (B)
Multitrack Rectangular-Manhattan plot in cases and controls. (C) The
distribution of DMCs according to the site regions of the
hypomethylation. (D) The distribution of DMCs according to the genomic
regions of the hypomethylation. (E) The distribution of DMCs according
to the site regions of the hypermethylation. (F) The distribution of
DMCs according to the genomic regions of the hypermethylation.
Most hypomethylated sites were located on chromosomes 2, 5, 6, and 7,
the most hypermethylated sites were on chromosomes 1, 2, and 6, and the
most overall differentially methylated sites were located on
chromosomes 1 and 6. The distribution of CpG sites in different regions
of genes is shown in [89]Supplementary Figures 2A,B.
Next, we carried out an analysis according to the functional domains of
DNA. Two hundred and fifty-one sites (50%) situated within 1,500 bp
upstream of the transcription start site (TSS 1500) among the
hypermethylated sites, followed by 100 sites situated within 200 bp
upstream of the transcription start site (TSS 200). Ninety-five sites
(19%) were located at the gene bodies. In addition, the smallest
percentage (0.02%) was in the 3′UTR for hypermethylated loci, while
mostly hypomethylated loci had the smallest percentage in the first
exon, 5′UTR and 3′UTR (0.04%) ([90]Figure 1C).
Among the hypomethylated sites, 120 sites were located at gene bodies
(50.0%), followed by 40 sites situated within 1,500 bp upstream of TSS
(19.4%) and 40 sites at noncoding intergenic domains (19.4%)
([91]Figure 1D).
When comparing the IS group to the control group, we observed that most
of the hypermethylated loci (42.59%) were found in CpG sites, while
most of the hypomethylated loci were in the open sea (70.57%)
([92]Figure 1E). The smallest percentage (4.33%) of hypomethylated loci
was in sites, compared to the smallest percentage of hypermethylated
sites (3.06%) found on shores ([93]Figure 1F). The distribution of DMCs
according to the island regions of the hypomethylation and
hypomethylation is shown in [94]Supplementary Figures 1A,B.
Bioinformatics analysis of differentially methylated genes of whole blood
from IS patients
The differentially methylated sites were analyzed concerning known
functional genes with the DAVID bioinformatics database. Among the 622
differentially methylated sites, the top 19 genes with the greatest
extent of hypermethylation were ABCA1, ADAMTSL5, COLgA2, ERCC5, TGFB1,
ABCG1, ATP10A, CYP2E1, HOX4A, PCDHB7, ARL4C, SULF2, KLF11, ADARB2,
GDFl5, CDHJ5, CAMTA1, SMG6, and RNFl44b ([95]Supplementary Figure 3).
After analyzing the methylation detection data of the target region,
especially the methylation level of the differential site, the
methylation levels of the 19 candidate genes in different CpG sites are
described in [96]Table 3. ABCA1, ADARB2, ATPI0A, CAMTA1, CDHI5, COL942,
and TGFB1 have differences in the overall methylation level of CpG
sites between the IS group and the healthy control group, and they are
statistically significant (at the same time satisfying the triple test,
that is, T-test P-value <0.05, U-test P-value < 0.05 and logistic
regression analysis P-value < 0.05). Among the above seven genes
(marketed ^* in [97]Table 3), the CpG site in the CAMTA1 promoter
region was hypermethylated in the IS case group. The CpG site of the
promoter region of six genes, ABCA1, ADARB2, ATPI0A, CDHI5, COL942, and
TGFβ1, was hypomethylated in the IS case group.
Table 3.
Genes with significant differences in CpG site methylation levels.
Genes P-value (T-test) R-value (U-test) R-value (logistic) OR (L95-U95)
(logistic) Methyl diff
ABCA1_1 0.1168 0.0876 0.1237 0.1100 (0.0066–1.8294) −0.00121
ABCA1_2^* 0.0050* 0.0032* 0.0102* 0.1234 (0.0246–0.6191) −0.00425
ABCG1_1 0.3874 0.3107 0.3769 1.2366 (0.7720–1.9806) 0.0045
ABCG1_2 0.7507 0.4457 0.7444 13221 (0.2467–7.0848) 0.00037
4DAMTSL5J 0.0725 0.0682 0.0825 1.9938 (0.9149–4.3449) 0.00586
ADA/fTSL5_2 0.1073 0.0391 0.1183 5.6142 (0.644148–0.9353) 0.00165
ADARB_1* 0.0161* 0.0328* 0.0299* 0.0982 (0.0121–0.7982) −0.00316
ADARB2_2 0.1428 0.0696 0.1456 0.4684 (0.686–1.3011) −0.00301
ARL4C_1 0.7746 0.7473 0.7679 1.8657 (0.0296–117.429) 0.00013
ARI,4C_2 0.1067 0.0732 0.1160 3.6092 (0.728247–0.8893) 0.00221
ATPIOA_1* 0.0264* 0.0045* 0.0415* 0.0612 (0.0042–0.8992) −0.00234
CAKfTAl1_1* 0.0286* 0.0348 0.Q387* 247764.8000 (1.9042–32
238134221.0000) 0.00042
CAMTAI_2 0.4899 0.2708 0.4799 0.0512 2 (0.0000–195.381) −0.00017
CAMTAI_3 0.6222 0.8309 0.6109 0.4530 (0.0214–9.5705) −0.00037
CAMTA1_4 0.7710 0.8287 0.7638 0.9715 (0.8043–1.1734) −0.0033
CDH15_1* 0.0408 0.0102 0.045 P 0.8533 (0.7257–1.0035) −0.03357
COL9A21^* 0.0151* 0.0589 0.0253* 0.0046 (0.0000–0.5159) −0.00125
COL9A2_2 0.5745 0.8063 0.5655 0.8485 (0.4844–1.4861) −0.00201
CYP2E1_1 0.1898 0.1760 0.1859 0.9616 (0.9074–1.0190) −0.04958
CYP2E1_2 0.4219 0.4773 0.4116 0.9807 (0.9360–1.0275) −0.03444
ERCC5_1 0.9926 0.3862 0.9925 0.9914 (0.1607–6.1143) −0.0000098
ERCC5_2 0.4109 0.5268 0.4012 5.0355 (0.1155–219.515) 0.00043
GDF15_1 0.5398 0.4304 0.5298 1.0709 (0.8649–1.3259) 0.00572
H0XA4_1 0.1381 0.1258 0.1570 1.0787 (0.9713–1.1980) 0.07174
HOXA4_2 0.0606 0.0927 0.0679 7.0837 (0.8654–57.9837) 0.00192
KLFU_1 0.7047 0.6891 0.7038 1.0456 (0.8308–13160) 0.00333
KLFU_2 0.4291 0.6146 0.4208 0.9219 (0.7563–1.1238) −0.00810
KLFU_1 0.0952 0.1989 0.1045 1.0586 (0.9882–1.1340) 0.05322
PCDHB7_1 3,606 0.3862 0.3534 0.9594 (0.8789–1.0472) −0.02123
PCDHB7_1 0.3076 0.4006 0.2946 0.9154 (0.7758–1.0800) −0.01267
RNF14-4B_1 0.0551 0.0451 0.0744 0.0263 (0.0005–1.4318) −0.00119
SMG6_1 0.8594 0.7864 0.8550 0.5022 (0.0003–812.876) −0.000047
SULF2_1 0.8628 0.8264 0.8586 0.8899 (0.2465–3.2125) −0.00027
SULF2_2 0.1106 0.0980 0.1169 0.5068 (0.2167–1.1854) −0.00406
SULF2_1 0.3171 0.0927 3,169 0.6329 (0.2583–1.5505) −0.00238
TGFβ1* 0.00/2* 0.0006* 0.0052* 0.5052 (031270–0.863) −0.01798
[98]Open in a new tab
“OR(L95-U95) (Logistic)” means the OR value of the regression
regression and 95% confidence interval: “MethylDif” means the degree of
difference in methylation between the two groups-the average degree of
methylation in the case group-the control group Average degree of
methylation.
We undertook the gene ontology and pathway analysis of 502
differentially methylated sites to categorize them according to their
biological functions and pathways. As [99]Figures 2A,B show, the KEGG
pathway analysis affiliated them with numerous pathways. The highest
number of genes participated in Cell Adhesion Molecules (CAMs) (142
genes, P = 0.0021). Morphine addiction and the Hippo signaling pathway
were involved in 92 (P = 0.028) and 154 (P = 0.042) genes,
respectively. The genes hypermethylated CpG mapped were involved in the
Hippo signaling pathway (154 genes, P = 0.014), Dorso-ventral axis
formation (27 genes, P = 0.028), and Arrhythmogenic Right Ventricular
Cardiomyopathy (ARVC) (74 genes, P = 0.032). The hypomethylated genes
were implicated in CAMs (142 genes, P = 0.0008), Allograft rejection
(37 genes, P = 0.005), and Graft-vs.-host disease (41 genes, P = 0.007)
(Figures 4D,E; [100]Supplementary Tables 4, [101]5).
Figure 2.
[102]Figure 2
[103]Open in a new tab
The bioinformatics results of 850K methylation microarray. (A,B) KEGG
analysis results of different genes in cases and controls. (C)
Comparison of DNA methylation differences of seven genes. (D)
Methylation levels of CAMTA1 gene in cases and controls (*P < 0.05).
(E) Comparison of the average methylation levels of 23 CpG sites in the
CAMTA1 gene promoter region between two groups (*P < 0.05). (F) CAMTA1
mRNA expression in the expanded population (**P < 0.01). (G) CAMTA1
protein expression in patients and healthy people. (H) Correlation
between CAMTA1 gene expression level and methylation level.
The GO pathway analysis results are presented in [104]Supplementary
Tables 6–[105]8. GO enrichment analysis in the categories of cellular
component (CC) revealed that 10 GO terms from CC were significantly
enriched in patients ([106]Figure 2D). The top three terms are listed
as follows: (1) early endosome membrane; (2) MHC class I protein
complex; (3) cell junction. Furthermore, homophilic cell adhesion via
plasma membrane adhesion molecules, cell adhesion, and phospholipid
translocation were the top three pathways in biological processing
systems (BP pathways). The differentially methylated genes were
arranged into 10 groups based on their molecular function (e.g.,
biochemical cascade) like calcium ion binding,
phospholipid-translocating ATPase activity, and collagen binding.
Validation of the differentially methylated CpG Loci in CAMTA1
After analyzing the methylation detection data of the target region,
especially the methylation level of the differential site, the
methylation levels of the 19 candidate genes in different CpG sites
were validated. ABCA1, ADARB1, ATP10A, CAMTA1, CDH15, COL9A2, and TGFβ1
were the top seven genes having significantly different methylation
levels ([107]Figure 2C). The CAMTA1 gene presents the most different
hypermethylation levels between the two groups ([108]Figure 2D).
Next, we explored the most common methylation sites of CAMTA1, located
on Chr1. We screened out a CpG site in the promoter region of the
CAMTA1 gene through website prediction. The sequence of this region is
as follows, with a total of 23 CpG sites, marked in red:
GACTCATGGTTGCCTGTCCCAGGATGAGGC
[MATH: CG
:MATH]
C
[MATH: CG
:MATH]
GC
[MATH: CG
:MATH]
AAAGCAGAGAAG
[MATH: CG
:MATH]
CCAGCCC
[MATH: CG
:MATH]
GC
[MATH: CG
:MATH]
CC
[MATH: CG
:MATH]
GGTGGAG
[MATH: CG
:MATH]
CTGGGCAGC
[MATH: CG
:MATH]
AGTTTCCCACCCTCCTCAATC
[MATH: CG
:MATH]
GAGAGTC
[MATH: CGCGCG :MATH]
GGGCTTTTCCTAATAAATAGCCAGGCACC
[MATH: CG
:MATH]
CTGCCCT
[MATH: CGCG :MATH]
CTG
[MATH: CG
:MATH]
TA
[MATH: CG
:MATH]
GGAGCACGTGCCCCC
[MATH: CG
:MATH]
GGAGGTGGG
[MATH: CG
:MATH]
CC
[MATH: CG
:MATH]
CCAGGTGCCCGAACGAGCCTAGGAACCGGGT
[MATH: CG
:MATH]
GGAACGAGCCTAGGAACCGGGTC.
We compared mean methylation levels at 23 CpG sites in the promoter
region of the CAMTA1 gene in the control and case groups. In the CAMTA1
display, 16 CpG and 21 CpG sites showed a significant difference (P <
0.05; [109]Figure 2E). We found elevated methylation levels in the
promoter region of the CAMTA1 gene in ischemic stroke patients, at the
same time with decreased CAMTA1 mRNA expression ([110]Figure 2F). As a
consequence, its protein level was also reduced in IS patients
([111]Figure 2G). To validate the correlation of DNA methylation with
gene expression, we analyzed the Pearson correlation between the degree
of methylation in the promoter region of the CAMTA1 gene and its
corresponding CAMTA1 mRNA expression level (P < 0.0001; [112]Figure
2H).
CAMTA1 knockout accelerated cell proliferation and inhibited apoptosis
To investigate the CAMTA1 function implicated in stroke, we did
knockout CAMTA1 gene in HEK 293T and SH-SY5Y cell lines by Crisper/Cas9
([113]Supplementary Figure 1D). The qPCR results showed that the mRNA
of CAMTA1 had decreased successfully ([114]Supplementary Figure 1E).
The HEK 293T cells are a well-established tool. Moreover, the SH-SY5Y
cell line was selected because it has a higher expression of CAMTA1
mRNA compared to other neural cells ([115]Supplementary Figure 1C). The
staining of cells with crystal violet dye showed that the number of
staining cells significantly increased in both CAMTA1-KO cells
([116]Figures 3A,B) due to the accelerated cell proliferation
([117]Figure 3C). Furthermore, the TUNEL assay demonstrated that
decreased CAMTA1 protected cells from oxygen-glucose
deprivation/reperfusion (OGD/R) injury, and the TUNEL positive cells
decreased in CAMTA1-KO cells compared after OGD/R ([118]Figure 3D).
Taken together, these results indicate that decreased CAMTA1 levels
could influence OGD/R injury.
Figure 3.
[119]Figure 3
[120]Open in a new tab
CAMTA1 KO influences cell proliferation. (A,B) Gentian Violet staining
shows differences in cell proliferation in HEK293T and SH-SY5Y cell
lines (**P < 0.01, ***P < 0.001). (C) Relative viabilities of HEK293T
and SH-SY5Y cells after incubation with OGD/R treatments (**P < 0.01).
(D) Cell apoptosis was detected by TUNEL staining (*P < 0.05, **P <
0.01, ***P < 0.001). ns stand for not statistically different.
The transcriptomic study found that CCND1 was upregulated in SH-SY5Y
CAMTA1-KO cell line
Next, we used the RNA-seq approach to analyze the transcriptomes of two
cell lines :SH-SY5Y and HEK293T. The genes having different expression
levels in two cell lines are shown in [121]Supplementary Table 9. We
performed GO term and KEGG pathway enrichment analysis to analyze this
signature further. From the KEGG enrichment results, it listed the top
six KEGG pathways of the hypermethylated genes: focal adhesion (31
genes, P = 0.0076), Hippo signaling pathway (26 genes, P = 0.0076),
cellular senescence (25 genes, P = 0.0121), p53 signaling pathway (15
genes, P = 0.0202), chronic myeloid leukemia (15 genes, P = 0.0308) and
ECM-receptor interaction (14 genes, P = 0.0446). The pathways of
hypomethylated genes are Ribosome (94 genes, P = 0.0076), Huntington's
disease (81 genes, P < 0.0001), Parkinson's disease (63 genes, P <
0.0001), Oxidative phosphorylation (60 genes, P < 0.0001), RNA
transport (63 genes, P < 0.0001), and Spliceosome (53 genes, P <
0.0001).
The GO analysis results are shown in the [122]Supplementary materials.
We expected to find some pathways to explain the excessive
proliferation of the CAMTA1 KO cells. The heat map of the genes
involved in the Hippo signaling pathway, cellular senescence pathways,
and p53 signaling pathway is presented in [123]Figures 4A–C. The heat
map of the whole genes is shown in [124]Figures 4D,E. These results
revealed that the CCND1 gene was upregulated and implicated in all
these pathways. Interestingly, the cell cycle analysis by flow
cytometry ([125]Figure 4F) demonstrated that more SH-SY5Y CAMTA1-KO
cells entered the S phase. As we know, the CCND1 gene codes cyclin D1
is an important cell cycle regulator that controls the transition from
G1 to the S phase. Meanwhile, many studies in mouse models and
different neural cell lines demonstrated that cyclin D1 level was
increased under ischemic stress conditions. Therefore, we suppose that
CAMTA1 could regulate cyclin D1, and through this intermediate CAMTA1
could play a role in stroke.
Figure 4.
[126]Figure 4
[127]Open in a new tab
RNA seq results of CAMTA1 KO SH-SY5Y cell lines and WT cell lines.
(A–C) Heatmap of P53 relative genes, senescence genes, and Hippo
pathways of the RNA seq in CAMTA1 KO SH-SY5Y cell lines. (D) Bubble
plot shows the significant GO pathways involved by the CAMTA1 KO
SH-SY5Y cell lines. (E) Bubble plot shows the significant KEGG pathways
involved by the CAMTA1 KO SH-SY5Y cell lines. (F) Flow cytometry
histograms of actively dividing and quiescent cells. The percent of
cells in each cell cycle phase is shown above the peaks (**P < 0.01,
***P < 0.001). ns stand for not statistically different.
CAMTA1 may regulate cyclin D1 to control the cell cycle
Next, the new cell line was generated in SH-SY5Y CAMTA1 KO cells with
siCCND1 ([128]Figure 5A). The result of the CCK-8 assay demonstrated
that decreased expression of the cyclin D1 reduced cell viabilities
compared to SH-SY5Y CAMTA1 KO cells ([129]Figure 5B). The crystal
violet staining was consistent with the cell viability data
([130]Figure 5C). The results illustrated that cyclin D1 regulates cell
proliferation induced by decreased CAMTA1. We also checked the cyclin
D1 expression in IS patients ([131]Figure 5D). Their expression is
markedly elevated in patients having lower CAMTA1 levels. The results
suggested that CAMTA1 controlled the cell cycle by regulating cyclin D1
expression in IS. As CAMTA1 is a transcription activator, a dual
luciferase reporter assay was generated to investigate whether CAMTA1
directly regulates cyclin D1 expression. In HEK293T cells, the
Firefly/Renilla ratio was not significantly changed with different
expressions of CAMTA1, compared to without CAMTA1 ([132]Figure 5E;
[133]Supplementary Figure 2F). It demonstrated that CAMTA1 could not
directly affect the CCND1 promoter region. There may be an intermediary
between them.
Figure 5.
[134]Figure 5
[135]Open in a new tab
The downregulation of CAMTA1 could promote cyclin D1 expression. (A)
CAMTA1 and cyclin D1 expressions in different SH-SY5Y cell lines. (B)
Relative cell viabilities in different SH-SY5Y cell lines after
incubation with OGD/R treatments (**P < 0.01). (C) Gentian Violet
staining shows differences in cell proliferation after the treatment in
different SH-SY5Y cell lines (**P < 0.01, ***P < 0.001). (D) CAMTA1 and
cyclin D1 expressions in IS patients. (E) In HEK293T KO cell, the
effect of CAMTA1 on CCND1 transcriptional activity was evaluated using
a luciferase reporter assay. (Statistical analysis of 3–6 independent
experiments under each condition is shown in the column chart, and the
error bar indicates ± 1 SD. *p < 0.05). ns stand for not statistically
different.
Discussion
Emerging evidence indicates that DNA methylation plays a role in the
pathogenesis of IS. In this context, we analyzed the genome methylation
levels from the peripheral blood samples of IS patients and healthy
controls using an 850K Bead Chip. Out of the 622 CpG sites showing
differential methylation, 80.4% (502 sites) exhibited hypermethylation,
and 19.6% (122 sites) indicated hypomethylation, which suggests
sufficient differences in the DNA methylation between patients and
controls. These different sites were traced to 278 genes. According to
our GO analysis and KEGG pathway analysis, they are mainly involved in
the following pathways and functions: adhesion of cell membrane,
adhesion molecules to homophilic cells, cell adhesion, phospholipid
transport, neurogenesis regulation, calcium ion binding,
collagen-binding, and sugar metabolism. All these pathways and
functions have been previously reported to be related to the ischemic
stroke (Love, [136]2003; Wen et al., [137]2005; Zündorf and Reiser,
[138]2011; Zhao et al., [139]2020).
A disadvantage of our experience is the sample size (n = 3); we
verified the top 19 differentially methylated genes: ABCA1, ADAMTSL5,
COL9A2, ERCC5, TGFBI, ABCG1, ATP104, CYP2EI, HOX44, PCDHB7, ARL4C,
KLFI1, SULF2, ADARB2, GDFI5, CDHI5, CAMTAI, SMG6, and RNF144b. Several
DNA methylation modifications in the pathogenesis of IS have been
investigated in the past few years. For example, the study in the
middle cerebral artery occlusion (MCAO) in a rat model showed DNA
methylation level of Na-K-Cl cotransporter 1 (NKCC1) was decreased
(Lagarde et al., [140]2015). Hu et al. found the hypermethylation of
thrombospondin 1 (THBS1), an angiostatic factor implicated in platelet
aggregation, in an in vitro model of stroke. IS patients presented high
plasma homocysteine levels associated with DNA hypermethylation of the
thrombomodulin (TM) (Stanzione et al., [141]2020). Hypermethylation of
cyclin-dependent kinase inhibitor 2B (CDKN2B), a gene involved in the
pathogenesis of calcification, was also demonstrated to relate to
calcification of the arteries in patients with IS (Unzeta et al.,
[142]2021). Baccarelli et al. suggested that the association between
the hypomethylation of long interspersed nucleotide elements (LINE-1)
and vascular cell adhesion protein 1 (VCAM-1) expression could be an
early event in the etiology of cerebrovascular diseases, including IS
(Lee et al., [143]2010). All these 19 genes have never been reported in
the IS field. Although they could be new candidate genes related to the
occurrence and development of stroke, we cannot rule out the
possibility that this is just one bias from our experiment. Even though
blood is commonly used in epigenomic studies, its heterogeneous nature
leads to interpretation difficulties. For future research on defining
their specific role in IS, we could verify their differential
methylation levels from a selected single cell type, for example,
lymphocytes.
We took CAMTA1 as the first candidate to be explored because its
methylation markedly differs between patients and controls. Recently,
Shen et al. also identified methylation modification of the CAMTA1 gene
in more than 400 IS patients (Shen et al., [144]2019). Furthermore, our
MetylTarget analysis found that the methylation levels of CpG16 and
CpG21 at the two sites in the IS group were significantly higher than
those in the healthy control. It suggests that the hypermethylation of
the CAMTA1 gene promoter might relate to ischemic stroke.
CAMTA1 gene was identified in 2003 as a candidate for tumor suppressor
in neuroblastoma (Henrich et al., [145]2006). The following year,
Nakatani et al. investigated the relationship between CAMTA1 expression
and cell cycle progression in N-type neuroblastoma SK-N-SH cells. They
suggested that CAMTA1 could play a role in cell cycle regulation. Many
studies examined its functions in various tumor cells, including breast
cancer, colon cancer, pheochromocytoma, neuroblastoma, and glioma. The
results showed that CAMTA1 could regulate tumor proliferation as an
antitumor gene (Katoh and Katoh, [146]2003; Kim et al., [147]2006;
Baccarelli et al., [148]2010a; Juhlin et al., [149]2015; Lu et al.,
[150]2018). Genetic studies of the WWTR1 (a protein known as
TAZ)-CAMTA1 were well established in epithelioid hemangioendothelioma
(EHE), a malignant vascular cancer. This discovery led WWTR1-CAMTA1
fusions to become useful diagnostic markers for EHE (He et al.,
[151]2021). The mechanistic basis of the oncogenic functions of the
TAZ-CAMTA1(TC) fusion protein has been distinctly defined. The fusion
of CAMTA1 drove the constitutive nuclear localization to TAZ, and they
escaped from the Hippo pathway regulation, rendering it constitutively
active (Asgharzadeh et al., [152]2012).
Consequently, cells expressing TC oncoprotein display a TAZ-like
transcriptional program that causes resistance to oncogenic
transformation (Phurailatpam et al., [153]2015). More recently, He et
al. demonstrated that CAMTA1 could regulate proliferation and the cell
cycle in glioma by inhibiting AKT phosphorylation (He et al.,
[154]2021). Nevertheless, the function of CAMTA1 in neurological
diseases is largely unknown. Just a few studies reported that the
CAMTA1 gene had been associated with neonatal neuroblastoma, ataxia
(Phurailatpam et al., [155]2015), and sporadic amyotrophic lateral
sclerosis (Liang et al., [156]2020).
Our research focused on studying the function of CAMTA1 in strokes. The
results showed that knockout of the CAMTA1 gene in SH-SY5Y cells
increased the proliferation and reduced the apoptosis after
oxygen-glucose deprivation/reoxygenation, and more cells entered the S
phase. The results revealed that the cell cycle was dysregulated in
CAMTA1 KO cells. Our findings were consistent with the previous studies
on tumor cells. CAMTA1 also plays an essential role in cell cycle
regulation in strokes. We examined the changes of the mRNA and protein
expression under CAMTA1 deletion to clarify its mechanisms in strokes.
The CCND1 mRNA and its protein cyclin D1 expression were significantly
increased in CAMTA1 KO cells. The functions of cyclin D1 are known for
regulating the cell cycle's progression through the G1 to S phase (Fu
et al., [157]2004). Increasing evidence demonstrates that dysregulation
of cell cycle machinery is implicated in strokes. Significantly, many
studies reported that cyclin D1 levels are increased in models of
cerebral ischemia (Cai et al., [158]2009; Baccarelli et al.,
[159]2010b; Zhou et al., [160]2016). Our data suggest CAMTA1 could
implicate IS through increasing cyclin D1. As we know, cyclin D1 could
be regulated by different signal pathways, like the Hippo pathway,
Jak/Stat pathway, etc. Our results established for the first time the
link between CAMTA1 and cyclin D1. However, the dual-luciferase assay
showed that there might not be a direct link between them. It is
supposed that CAMTA1 could affect one intermediary protein that induces
the cyclin D1 expression. CCND1 gene is a downstream transcriptional
target of the Hippo pathway, which was affected in SH-SY5Y CAMTA1 KO
cells. One possibility is that a specific protein implicated in the
Hippo signaling pathways could be regulated by CAMTA1. One limitation
in our work is that we used SH-SY5Y cells, which is broadly studied for
elucidating molecular mechanism in IS pathogenesis field. It's a
neuroblastoma cell line, and we should explore the effect of CAMTA1 in
a mouse model. CAMTA1 is principally expressed in the brain. For future
study, we could decrease the expression of CAMTA1 in mouse brain and
then study its effect under ischemic stress conditions.
In conclusion, (1) our study helped identify new candidate genes for
the pathogenesis of IS. We found 19 genes with significant DNA
methylation modifications in IS patients. All of them are involved in
the pathways related to stroke. However, the sample size is our
limitation. In future studies, this result should be validated using a
large-scale sample size and selecting a single cell type (to avoid the
bias due to the heterogeneous compositions from blood samples), and
then define their specific functions in strokes. (2) Out of 19 genes,
we concentrated on studying the role of CAMTA1 in Stroke because of its
most different DNA methylation levels between healthy people and IS
patients. Cell experiences demonstrated that CAMTA1 could affect cell
proliferation and cell cycle in normal conditions or in the OGD/R
model. (3) Moreover, decreased CAMTA1 could raise cyclin D1 levels. Our
results showed for the first time that CAMTA1 plays a role in strokes
by regulating cyclin D1, which increased under ischemic stress
conditions. However, the mechanism by which CAMTA1 regulates cyclin D1
is presently unclear. As our result shows, there is no direct link
between them; identifying their intermediary using a different neuron
cell line and in vivo models could form part of future studies.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found below: [161]https://www.ncbi.nlm.nih.gov/geo/
[162]GSE197080, [163]GSE197081, and [164]GSE205687.
Ethics statement
The studies involving human participants were reviewed and approved by
the Institutional Ethics Board of The First Affiliated Hospital of the
Henan University of Chinese Medicine. The patients/participants
provided their written informed consent to participate in this study.
Author contributions
HZ and YHe designed the experiments. YLi and GS carried out most of the
experiments. JJ, XZ, and FL analyzed the experimental results. CZ and
YLi wrote the manuscript. ZL, YX, ZZ, SY, BZ, YLu, YHu, YP, and TH
participated in the discussion of this project. All authors contributed
to the article and approved the submitted version.
Funding
This study was supported by the National Natural Science Foundation of
China (Nos. U2004114 and 81571154).
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[165]https://www.frontiersin.org/articles/10.3389/fncel.2022.868291/ful
l#supplementary-material
[166]Click here for additional data file.^ (502.5KB, pdf)
References