ERK1/2 gene expression and hypomethylation of Alu and LINE1 elements in patients with type 2 diabetes with and without cataract: Impact of hyperglycemia‐induced oxidative stress

Abstract

Aims
This study aimed to delineate the effect of hyperglycemia on the Alu/LINE‐1 hypomethylation and in ERK1/2 genes expression in type 2 diabetes with and without cataract.

Methods
This study included 58 diabetic patients without cataracts, 50 diabetic patients with cataracts, and 36 healthy controls. After DNA extraction and bisulfite treatment, LINE‐1 and Alu methylation levels were assessed using Real‐time MSP. ERK1/2 gene expression was analyzed through real‐time PCR. Total antioxidant capacity (TAC), and fasting plasma glucose (FPG) were measured using colorimetric methods. Statistical analysis was performed with SPSS23, setting the significance level at P < 0.05.

Results
The TAC levels were significantly lower for cataract and diabetic groups than controls (259.31 ± 122.99, 312.43 ± 145.46, 372.58 ± 132.95 nanomole of Trolox equivalent) with a significant correlation between FPG and TAC levels in both the cataract and diabetic groups (P < 0.05). Alu and LINE‐1 sequences were found to be statistically hypomethylated in diabetic and cataract patients compared to controls. In these groups, TAC levels were directly correlated with Alu methylation (P < 0.05) but not LINE‐1. ERK1/2 gene expression was significantly higher in diabetic and cataract patients, showing increases of 2.41‐fold and 1.43‐fold for ERK1, and 1.27‐fold and 1.5 for ERK2, respectively. ERK1 expression correlated significantly with FPG levels. A reverse correlation was observed between TAC levels and ERK1/2 expression.

Conclusions
Our findings indicate that hyperglycemia‐induced oxidative stress may alter ERK1/2 gene expression patterns and induce aberrant hypomethylation in Alu and LINE‐1 sequences. These aberrant changes may play a contributing role in diabetic complications such as cataracts.

Full text

ERK1/2 gene expression and hypomethylation
of Alu and LINE1 elements in patients with
type 2 diabetes with and without cataract:
Impact of hyperglycemia-induced
oxidative stress
Elham Zeinali Nia
1
, Ruhollah Najjar Sadeghi
2
*,MostafaEbadi
1
, Mohammad Faghihi
3
1
Department of Biochemistry, Faculty of Basic Sciences, Islamic Azad University Damghan Branch, Damghan, Iran,
2
Department of Clinical Biochemistry, Faculty of Medicine,
Mazandaran University of Medical Sciences, Sari, Iran, and
3
Department of Medical Sciences, Shahid Beheshti University, Tehran, Iran
Keywords
Diabetic cataract, DNA methylation,
Erk1/2
*Correspondence
Rouhallah Najjar Sadeghi
Tel.: 9833543089
Fax:9833543764
E-mail address:
najjarsadeghi@yahoo.com and r.
nsadeghi@mazums.ac.ir
J Diabetes Investig 2025
doi: 10.1111/jdi.14405
ABSTRACT
Aims:This study aimed to delineate the effect of hyperglycemia on the Alu/LINE-1
hypomethylation and in ERK1/2 genes expression in type 2 diabetes with and without
cataract.
Methods:This study included 58 diabetic patients without cataracts, 50 diabetic patients
with cataracts, and 36 healthy controls. After DNA extraction and bisulfite treatment, LINE-
1andAlumethylationlevelswereassessedusingReal-timeMSP.ERK1/2geneexpression
was analyzed through real-time PCR. Total antioxidant capacity (TAC), and fasting plasma
glucose (FPG) were measured using colorimetric methods. Statistical analysis was
performed with SPSS23, setting the significance level at P<0.05.
Results:The TAC levels were significantly lower for cataract and diabetic groups than
controls (259.31 –122.99, 312.43 –145.46, 372.58 –132.95 nanomole of Trolox equivalent)
with a significant correlation between FPG and TAC levels in both the cataract and
diabetic groups (P<0.05). Alu and LINE-1 sequences were found to be statistically
hypomethylated in diabetic and cataract patients compared to controls. In these groups,
TAC levels were directly correlated with Alu methylation (P<0.05) but not LINE-1. ERK1/2
gene expression was significantly higher in diabetic and cataract patients, showing
increases of 2.41-fold and 1.43-fold for ERK1, and 1.27-fold and 1.5 for ERK2, respectively.
ERK1 expression correlated significantly with FPG levels. A reverse correlation was
observed between TAC levels and ERK1/2 expression.
Conclusions:Our findings indicate that hyperglycemia-induced oxidative stress may
alter ERK1/2 gene expression patterns and induce aberrant hypomethylation in Alu and
LINE-1 sequences. These aberrant changes may play a contributing role in diabetic
complications such as cataracts.
INTRODUCTION
Diabetes mellitus has emerged as one of the leading cause of
mortality in the world and also, a significant socioeconomic
and health issue
1
. Overall, its devasting macrovascular and
microvascular complications such as cardiovascular disease,
kidney disease, retinopathy, and neuropathy contribute to
decreased quality of life for affected individuals
2
. Cataract is
one of the most common causes of visual impairment in type
2 diabetic patients
3
. Despite global efforts to delineate the
molecular basis of these complications of Diabetes mellitus and
to discover effective preventive and/or therapeutic solutions,
progress in this area has been limited.
Received 30 August 2024; revised 19 November 2024; accepted 2 January 2025
ª2025 The Author(s). Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd
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1
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution
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ORIGINAL ARTICLE

According to several lines of evidence, elevated blood glucose
levels can amplify the generation of free radicals and oxidative
stress, thereby impacting various cellular processes associated
with cell proliferation, including the mitogen-activated protein
kinases (MAPKs) pathways
4
.
It was shown that the MAPK signaling pathway plays an
essential role in the development and progression of some dis-
eases such as cataracts
5
, neurodegenerative diseases
6
,sometypes
of cancers
7
, and also diabetes
8
and its complications such as
diabetic nephropathy
9
, cardiomyopathy
10
.
The extracellular signal-regulated kinase 1/2 (ERK), a mem-
ber of the MAPK family, plays a central role in signaling cas-
cades from extracellular stimuli such as epidermal growth
factor to intracellular targets
11
.
The MAPK cascades typically consist of heterodimeric Ras
activation by extracellular signals, followed by a sequential of
phosphorylation events that lead to activation of Raf, MEK,
and ultimately ERK 1/2
7
. Downstream, activated ERK phos-
phorylates various cytosolic substrates
12
, serving as a central
hub in the regulation of fundamental processes, for example,
cellular growth, proliferation, differentiation, migration, survival,
and some stress responses
13
. Furthermore, some observations
suggest that ERK is involved in gene transcription either
directly or indirectly through the phosphorylation of the ribo-
somal protein S6 kinases
14
.
The ERK pathway is activated by a number of stimulators,
such as growth factors, cytokines, viruses, oncogenes, oxidation
stress, DNA damage, and p53 activation
8, 13
.
Increased expression or activation of the ERK signaling path-
way can result in tumorigenesis
13
, loss of cell cycle control
15
and apoptosis
16
.
It is possible that oxidative stress products elevate the levels
of oxidized glutathione, which could influence epigenetic pro-
cesses, including DNA and histone methylation by restricting
the availability of S-adenosylmethionine and consequently lead
to DNA hypomethylation
17
.
Aberrant DNA methylation is closely associated with various
complex and chronic diseases
18
and plays a vital role in regulat-
ing gene expression, genomic imprinting
19
, and notably, the
suppression of transposable elements such as LINE-1 and Alu
elements
20
.
Alu and LINE-1 hypomethylation has been reported in
aging
21
and some age-associated non-communicable diseases
22
such as hypertension
23
, during postmenopause
24
, and coronary
heart diseases
25
.
Changes in the methylation of Alu or LINE-1 elements dur-
ing growth and aging can lead to DNA damage and
double-stranded DNA breaks and loss of genomic integrity
26
.
It seems that elevated blood glucose levels, leading to
increased oxidative stress and ERK pathway activation, may
potentially affect epigenetic modifications. This cascade of
events could culminate in cellular senescence through metabolic
alterations and tissue impairment, as observed in the complica-
tions associated with type 2 diabetes mellitus.
Despite existing research, significant gaps remain in under-
standing the interplay between Alu and LINE-1 sequence meth-
ylation, oxidative stress, and the status of the ERK1/2 pathway
within diabetic populations, as well as their potential implica-
tions for diabetes-related complications such as cataract.
Consequently, the present study aims to address some of these
deficiencies and to evaluate the methylation profiles of Alu and
LINE-1 repetitive sequences, the expression levels of ERK1/2, and
their correlation with oxidative stress levels in leukocytes obtained
from diabetic patients with or without cataracts.
MATERIALS AND METHODS
Study design
Ethics Committee of Mazandaran University of Medical Sci-
ences approved this study (IR.MAZUMS.REC. 1398.D114).
Prior to involvement, all participants were informed about the
study’s objectives and considered competent to make the deci-
sion voluntarily. Blood samples were collected from all partici-
pants, including diabetic patients with and without cataracts as
well as healthy individuals admitted to the hospital for a gen-
eral check-up, and processed immediately.
Biochemical assays of blood glycemic profile
Fasting plasma glucose (FPG) and oral glucose tolerance test
(OGTT) were assessed using the glucose oxidase –peroxidase
method (GOD-PAP assay kit; Pars Azmun Co., Tehran, Iran).
Measurements of HbA1c levels were conducted utilizing enzy-
matic assay kits (Pishtazteb Diagnostic, Tehran, Iran). These
assays involved colorimetric procedures and were performed
using an autoanalyzer to ensure accurate and precise
measurements.
DNA extraction from peripheral blood cell and bisulfite
treatment
DNA extraction from blood samples was performed using a
QIAamp DNA blood mini kit (Qiagen, Hilden, Germany). For
bisulfite treatment, extracted DNA was subjected to the epiTect
bisulfite kit (Qiagen).
Relative quantification of LINE-1 and Alu methylation
The relative methylation levels of LINE-1 and Alu sequences
were determined using Step One Plus Real-Time PCR System
(Applied Biosystems) according to the IDLN-MSP method as
described by Santourlidis et al.
27
. In summary, bisulfite-treated
DNA was used as a template for the real-time amplification of
CpG-rich regions within LINE-1 and Alu consensus sequences,
employing specific primers that included at least two CpG
dinucleotides. (Alu-m and LINE-1m). Amplification of an adja-
cent region without CpGs (Alu-n and LINE-1n) was used to
normalize the amount of LINE-1/Alu DNA methylation
(Table 1).
The amplification protocol included an initial denaturation
step at 95°C for 10 min, followed by 40 cycles consisting of
denaturation at 95°C for 30 s, annealing temperatures (Table 1)
2
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ORIGINAL ARTICLE
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for 40 s, and extension at 72°C for 15 s. To calculate the relative
DNA methylation levels, the ΔC
T
method was employed along
with the formula 2ΔCt
27
.
RNA extraction from peripheral blood cell
RNA extraction was performed using a total RNA Purification
Mini kit (Favorgen Company, Taipei, Taiwan) following the
provided instructions.
RT-PCR and real-time quantitative PCR for ERK1/2 expression
cDNA synthesis was conducted using SuperScript™II (Invitro-
gen, Carlsbad, USA) and oligo(dT) following the manufacturer’s
protocol.
PCR primers were designed based on GenBank RNA
sequences for ERK1/2 genes, along with GAPDH as the house-
keeping gene (Table 2). The relative gene expression was calcu-
lated according to the 2ΔCtmethod.
The RT-PCR reaction mixture comprised the SYBR Green
Master Mix (1×), cDNA (5 ng/μL), each primer (0.2 μM), Pas-
sive Reference Dye (1×), and nuclease-free water (up to 20 μL).
The optimal amplification conditions were set as follows: Ini-
tial PCR activation step at 95°C for 10 min, 40 cycles of dena-
turation step at 95°Cfor15s,annealingat60°C and extension
at 72°Cfor40s.
Measurement of plasma total antioxidant capacity
The plasma total antioxidant capacity (TAC) was assessed using
the CUPRAC method (TAC assay kit; Kiazist Company, Karaj,
Iran). The total antioxidant capacity was then expressed as the
nanomole of Trolox equivalent antioxidant capacity.
Statistical analysis
The data analysis was conducted using SPSS software (version
23; SPSS Inc., Chicago, IL, USA) and Prism software version 6.
Initially, the quantitative data were assessed for normality using
the one-sample Kolmogorov–Smirnov test, and then, appropri-
ate statistical tests were selected. For parametric data, the inde-
pendent t-test was employed, while nonparametric data were
analyzed using the Mann–Whitney Utest. The Chi-square test
was employed to compare qualitative data. A statistically signifi-
cant threshold was set at a P-value less than 0.05.
RESULTS
Characteristics of the study population
In this study, a total of 58 diabetic patients without cataracts, 50
diabetic patients with cataracts, and 36 control individuals were
enrolled. Table 3shows the characteristics of the studied groups
with respect to age and gender. The age of the diabetic group,
cataract group, and control group did not show any significant
Table 1 | Primers design to evaluate the methylation status of Alu and LINE-1 sequences.
Genes Forward primer Reverse primer Annealing temperature (°C)
Alu-m 50ATTTTAGTATTTTGGGAGGTCGAGGC3
050GCAATCTCGACTCACTACAAACTCCG 3058
Alu-n 50GGGTGGATTATGAGGTTAGGAGAT 3050CATTCTCCTACCTCAACCTCCC 3061
LINE-1 m 50GCGCGAGTCGAAGTAGGGC3
050CTCCGACCAAATATAAAATATAATCTCG 3053
LINE-1- n 50AGGTTTTATTTTTGGGGGTAGGGtATAG3050CCCCTACTAAAAAATACCTCCCAATTAAAC 3060
The CpG dinucleotides in each primer, as well as the modified C, are shown as underlined.
Table 2 | Primers sequences used to evaluate the expression of ERK2 and ERK1 genes
Genes Forward primer Reverse primer Annealing temperature (°C)
ERK2 50TCCACCTTGACATGATGGGT 3050GGCACCAACAGTACAAAGCA 3055.3
ERK1 50CCAACCTGCTCATCAACACC 3050GAAGATGGGCCGGTTAGAGA 3054.5
GAPDH 50GTCTCCTCTGACTTCAACAGCG 3050ACCACCCTGTTGCTGTAGCCAA 3055.8
Table 3 | Demographic data of the patients and controls
Cataract Diabetic Control Comparison (P-value)
Cataract vs control Diabetic vs control Cataract vs diabetic
Number 50 58 36
Age (mean –SD) 61.62 –10.9 57.83 –10.07 58.47 –13.46 0.23
†
0.78
†
0.063
†
Age (range) 38–83 32–80 35–82
Gender Male 22 (45%) 26 (45%) 21 (58.3%) 0.351
‡
Female 28 (55%) 32 (55%) 15 (41.6%)
†
Independent T-test.
‡
Chi-square test.
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http://wileyonlinelibrary.com/journal/jdi Hyperglycemia, ERK1/2 expression, Alu/LINE1 methylation in type 2 diabetes

differences. Moreover, there were no statistically significant dif-
ferences in gender distribution among the studied groups.
Profile of blood glucose components
There was a significant difference in the levels of fasting plasma
glucose between the diabetic and cataract groups in comparison
to the control subjects (P<0.01). However, the fasting plasma
glucose levels did not differ significantly between the diabetic
and cataract groups (Table 4,P=0.82).
The mean of the plasma glucose defined by OGTT was
found to be statistically different between the diabetic and cata-
ract groups in comparison to that of control (P<0.01). None-
theless, the difference in OGTT levels between diabetic and
cataract groups was not significant (P=0.9).
In addition, the difference between the HbA1c level of the
diabetic and cataract groups in comparison to the control
group was significant (P<0.01). However, the difference
between diabetic and cataract groups was not statistically signif-
icant (P=0.1).
Evaluation of the total antioxidant capacity
The difference between the total antioxidant levels of the dia-
betic (P=0.042) and cataract groups (P<0.01) in comparison
to the control group was significant. Moreover, the difference
between diabetic and cataract groups was also significant
(P=0.043) (Table 5).
Evaluation of methylation status of the Alu sequences
The relative methylation level of the Alu sequences in both dia-
betic and cataract groups was significantly lower than that of
the control group (P<0.0001). Furthermore, assignificant dif-
ference was observed in the methylation levels of the Alu
sequence between cataract and diabetic groups (P<0.0001)
(Table 6).
Evaluation of methylation status of the LINE-1 sequences
While there was a significant difference between the methyla-
tion levels of LINE-1 sequences of cataract cases and controls,
this difference was not statistically significant for comparison of
diabetic and control groups. Additionally, the comparison of
LINE-1 methylation levels between cataract and diabetic groups
showed a significant difference (Table 7).
Evaluation of ERK2 gene expression
The expression of the ERK2 gene in both diabetic and cataract
groups exhibited a significant increase compared to the control
group by 1.27 and 1.5 times, respectively (P<0.01). The differ-
ence between the diabetic and cataract groups was also, statisti-
cally significant (P<0.01) (Table 8).
Table 4 | Data of blood glucose assessment components
Cataract Diabetic Control Comparison (P-value)
Cataract vs control Diabetic vs control Cataract vs diabetic
Number 50 58 36
FPG (mean –SD): mg/dL 169.24 –71.63 166.10 –70.66 84.86 –9.18 <0.01
†
<0.01
†
0.82
‡
OGTT (mean –SD): mg/dL 215 –99.39 218 –91.07 102.68 –14.5 <0.01
‡
<0.01
‡
0.9
‡
HbA1c (mean –SD): % 7.17 –0.24 7.57 –0.2 5.47 –0.18 <0.01
†
<0.01
†
0.10
‡
†
Mann-Whitney test.
‡
Independent sample T-test.
Table 5 | Total antioxidant capacity in each group
†
Mean –SD Range Cataract vs control Diabetic vs control Cataract vs diabetic
Cataract 259.31 –122.99 101.26–557.08 <0.01
‡
0.042
‡
0.043
‡
Diabetes 312.43 –145.46 82.46–621.67
Control 372.58 –132.95 125.83–715.42
†
Data was expressed as nanomole of Trolox equivalent/mL.
‡
Independent sample T-test.
Table 6 | Relative methylation levels (2ΔCt)ofAlusequencesin
different groups
Mean –SD
†
Range Cataract
vs
control
Diabetic
vs
control
Cataract
vs
diabetic
Cataract 0.28 –0.098 0.015–0.5 <0.01
‡
<0.01
‡
0.001
‡
Diabetes 0.36 –0.14 0.044–0.67
Control 0.47 –0.17 0.044–0.75
†
Mean of 2ΔCt.
‡
Independent sample T-test.
4
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ORIGINAL ARTICLE
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Evaluation of ERK1 gene expression
The expression of the ERK1 gene significantly increased in both
diabetic and cataract subjects in comparison to that of the con-
trol group, showing a 2.41-fold and 1.43-fold increase, respec-
tively (P<0.05). Notably, the difference in ERK1 gene
expression between the diabetic and cataract sample groups
was also statistically significant (P<0.01) (Table 9).
The correlation between FPG and TAC was examined using
the Pearson’s correlation test. A significant correlation was
observed between FPG and TAC levels in both the cataract
and diabetic groups (P<0.01). In contrast, no significant corre-
lation was identified between FPG and TAC levels in the con-
trol group (P≥0.05) (Figure 1).
The relationship between FPG with ERK1 and expression level
As seen in Figure 2, there was a significant correlation between
the levels of FPG and the expression of ERK1 in diabetic and
cataract patients, as determined by the Pearson test. However,
in the control group, there was no significant correlation
between FPG and ERK1 (P=0.409).
AsshowninFigure3, there was no statistically significant
correlation between the levels of FPG and the expression of
ERK2 in diabetic and cataract patients as well as in control
individuals.
The relationship between TAC with ERK1 and ERK2
expression level
In cataract and diabetic patients, a significant correlation was
observed between TAC levels and ERK1 gene expression
(P<0.01). Conversely, no significant correlation was found
between TAC levels and ERK3 expression in the control group
(P=0.633), as illustrated in Figure 4.
In diabetic and cataract patients, there was a significant cor-
relation between the level of TAC and the expression of ERK2,
asshowninFigure5(P=0.023, P<0.01 respectively). How-
ever, in control, no significant correlation was found between
TAC and ERK2 (P=0.356).
The relationship between TAC with Alu and LINE-1
methylation level
Using the Spearman’s test, in diabetic and cataract patients, a
significant correlation (P<0.01) was observed between Alu
methylation level and TAC level, as shown in Figure 6.How-
ever, in control, no significant correlation was found between
Alu methylation and TAC levels (P=0.927). There was no
correlation between LINE-1 methylation levels and TAC levels
in diabetic and cataract patients and control patients (Pearson’s
test) (P>0.05) (Figure 7).
The relationship between age with Alu and LINE-1
methylation level
Using the spearman test, we showed that there is no significant
correlation between age and methylation levels of both Alu
(Figure 8)andLINE-1(Figure9) sequences in cataract, diabe-
tes, and control groups.
DISCUSSION
Diabetes, a complex and chronic metabolic disease, is character-
ized by elevated blood glucose levels due to relative insulin defi-
ciency and/or insulin resistance, which over time leads to some
complications affecting multiple organs
28
.Althoughitshealth
consequences are well-known, the molecular mechanisms of
these complications are not yet fully elucidated.
Given the known interplay among blood glucose levels, oxi-
dative stress, gene expression, and epigenetic modifications
29
,
the present work investigates the relationship between blood
glucose levels, global genome methylation status, levels of oxida-
tive stress, and the expression of ERK1/2 genes.
According to our findings, the difference in gender distribu-
tion was not statistically significant. Biological and psychological
factors as well as the level of industrialization in the studied
populations, may contribute to some contradictory evidence
30
.
Table 8 | Relative ERK2 expression (2ΔCt) in different groups
Mean –SD Range Cataract
vs
control
Diabetic
vs
control
Cataract
vs
diabetic
Cataract 0.749 –0.279 0.388–1.354 <0.01
†
<0.01
†
0.01
‡
Diabetes 0.616 –0.175 0.343–10.76
Control 0.498 –0.26 0.169–1.173
†
Mann-Whitney test.
‡
Independent sample T-test.
Table 9 | Relative ERK1 expression (2ΔCt) in different groups
Mean –SD Range Cataract
vs
control
Diabetic
vs
control
Cataract
vs
diabetic
Cataract 0.131 –0.024 0.023–0.223 <0.01
†
<0.01
†
<0.01
†
Diabetes 0.219 –0.012 0.211–0.288
Control 0.091 –0.021 0.06–0.169
†
Mann-Whitney test 3.8 assessment of the relationship between FPG
and TAC levels.
Table 7 | Relative methylation levels of LINE-1 sequences in different
groups
Mean –SD
†
Range Cataract
vs
control
Diabetic
vs
control
Cataract
vs
diabetic
Cataract 0.458 –0.169 0.213–5.31 0.04
‡
0.9
‡
0.047
‡
Diabetes 0.54 –0.22 0.231–4.77
Control 0.534 –0.172 0.301–0.694
†
Mean of 2ΔCt.
‡
Independent sample T-test.
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y = -1.48x + 558.27
R² = 0.5169
Pearson Correlation = - 0.719
P value = <0.01
0
100
200
300
400
500
600
700
0 50 100 150 200 250 300 350 400
TAC
FPG
(b) Diabetic
y = 2.3051x + 176.98
R² = 0.0248
Spearman correlaon = 0.194
P value = 0.258
0
100
200
300
400
500
600
700
800
60 70 80 90 100 110
TAC
FPG
(c) Control
y = -1.3046x + 477.58
R² = 0.6058
Pearson Correlation = - 0.781
P value = <0.01
0
100
200
300
400
500
600
700
0 100 200 300 400 500
TAC
FPG
(a) Cataract
Figure 1 | Correlation between FPG and TAC based on the Pearson’s Correlation test in cataract (a), diabetic (b), and control (c). Significance of
correlation coefficients (r)wassetatP<0.05.
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y = -0.0356x + 13.129
R² = 0.5151
Pearson Correlaon = -0.735
P value < 0.01
-4.00
-2.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 100 200 300 400 500
ERK1 Expression(ΔCT)
FPG
(a) Cataract
y = -0.0422x + 14.498
R² = 0.6223
Pearson Correlaon = - 0.789
P value < 0.01
-5.00
0.00
5.00
10.00
15.00
20.00
0 100 200 300 400
ERK1 expression (ΔCT)
FPG
(b) Diabetic
y = -0.0469x + 13.138
R² = 0.0137
Spearman correlaon = - 0.142
P value = 0.409
-5.00
0.00
5.00
10.00
15.00
20.00
60 70 80 90 100 110
ERK1 expression (Δ CT)
FPG
(c) Control
Figure 2 | Correlation between FPG and ERK1 in cataract (a), diabetic (b), and control (c) samples. Significance of correlation coefficients (r)wasset
at P<0.05.
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y = 0.0023x + 1.1786
R² = 0.0107
Pearson Correlaon = 0.103
P value = 0.474
-4
-3
-2
-1
0
1
2
3
4
5
6
0 100 200 300 400 500
ERK2 expression(ΔCT)
FPG
(a) Cataract
y = 0.0031x + 1.2951
R² = 0.013
Spearman Correlaon = 0.115
P value = 0.392
-2
-1
0
1
2
3
4
5
6
0 100 200 300 400
ERK2 expression(ΔCT)
FPG
(b) Diabetic
y = 0.009x + 1.922
R² = 0.003
Spearman correlaon = 0.122
P value = 0.477
-2
-1
0
1
2
3
4
5
6
60 70 80 90 100 110
ERK2 expression (ΔCT)
FPG
(c) Control
8
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ORIGINAL ARTICLE
Zeinali Nia et al. http://wileyonlinelibrary.com/journal/jdi

In young and middle-aged populations
31
and also, in Canadian
society, men are more likely to have type 2 diabetes than
women
32
. However, women experience a greater increase in
postprandial hyperglycemia with age than men
30
, leading to an
increased prevalence of undiagnosed diabetes
33
.
As expected, blood glucose levels, as measured by FPG,
OGTT and HbA1c, were higher in both the diabetic and cata-
ract groups compared to those in the control group. In general,
the incidence of cataract is higher in diabetic patients than nor-
mal people, particularly in those with elevated blood glucose
level
34
.
The total antioxidant capacity of the cataract and diabetic
groups was significantly lower than that of the control group.
In addition, there was a reverse correlation between FPG and
TAC levels in diabetic and cataract patients.
Chronic hyperglycemia and increased glucose oxidation
induce excessive production of mitochondrial superoxide radi-
cals and potentiate prooxidative pathways such as polyol
pathways
35
, formation of advanced glycation end products
36
,
activation of protein kinase C pathways
37
and dynamic changes
in mitochondrial morphology and fragmentation
38
.Onthecon-
trary, hyperglycemia-induced oxidative stress can also disrupt
the signaling pathway involved in insulin secretion by pancre-
atic islet beta cells
39
.
Extensive activation of these pathways in turn, lead to sub-
stantial depletion of antioxidant defense systems, which ulti-
mately intensifying cellular oxidative stress in the development
of various diseases, including diabetes
40
.
Contrary to the findings of the present study and reports
mentioned above, Azizi Soleimani and Hazal Tuzcu
41, 42
found
no significant correlation between oxidative stress status and
serum glucose levels. These discrepancies can be attributed to
the evaluation of different markers of oxidative stress status,
that is, malondialdehyde
41
and protein carbonyls
42
.
Subjects with diabetes and cataracts had significantly lower
relative methylation levels of Alu sequences than controls. The
methylation levels of LINE-1 sequences in the control group
exhibited a significant difference compared to those of patients
with cataracts and diabetes. However, no significant difference
in LINE-1 methylation levels was observed between the patients
suffering from cataracts and those with diabetes.
Consistent with our findings, it was revealed that in type 2
diabetic patients with poorly controlled carbohydrate metabo-
lism, the reduced levels of LINE-1 and Alu methylation are
directly related to increased FPG and HbA1C levels as well as
worsening metabolic status
43–46
.
Some researchers assessed the relationship between Alu and
LINE-1 methylation levels and insulin resistance, so that
Carraro et al.
47
and Zhao et al.
48
showed a positive correlation
between the methylation of LINE-1 and Alu with HOMA-IR
index.
Our findings indicate that there is no significant correlation
between age and the methylation levels of LINE-1 or Alu ele-
ments. However, literature presents conflicting evidence regard-
ing the relationship between aging and genome
hypomethylation in these regions. Accordingly, studies utilizing
blood samples have demonstrated that while Alu methylation
significantly decreases with age, while LINE-1 methylation levels
remain stable
49–51
.
We could not find any significant correlation between age
and LINE-1 or Alu methylation. With regards to aging and
genome hypomethylation at LINE-1 or Alu elements, there are
some conflicting reports. Accordingly, using blood samples, it
was found that Alu methylation significantly decreases during
aging whereas LINE-1 methylation does not
10, 33, 51
.
In contrast, Lars Erichsen et al.,
52
identified an age associated
hypomethylation of Alu and LINE-1 elements in cell-free
DNA, but not for cellular DNA from peripheral blood.
There are thousands of copies of Alu and LINE-1 elements,
however because of truncation and losing most of the CpG
dinucleotide containing 50UTR in LINE-1 sequences, Alu
methylation may represent genomic methylation more than
LINE-1, consequently genomic stability
53
.
To explain these discrepancies, we must consider
inter-cohort variations and also the different regions of the
genome for studying methylation. In addition, only some types
of repetitive sequences or just a set of specificCpGslosemeth-
ylation at certain ages
4, 5
. The methylation status of some CpGs
was found to be influenced by SNP
6
,smokingandhost
characteristics
26
, and the location of repeated sequences
location
25
. Finally, there may be several mechanisms of global
methylation. However, not all of these mechanisms are
age-dependent
54
.
Different methylation patterns of Alu and LINE-1 elements
had previously been reported in some conditions such as
autism spectrum disorder, in which LINE-1 elements were sig-
nificantly hypermethylated in young and middle-aged subfam-
ilies, while Alu elements were significantly hypermethylated in
old and middle-aged subfamilies
55
and also in Behcet’sDisease
(a multisystem chronic inflammatory disease) herein for Alu
hypomethylation, significant difference was observed between
patients and controls, and also, inactive patients vs controls.
However, no statistically significant differences were detected
for LINE-1
39
.Furthermore,ithasbeenshownthatexposureto
electromagnetic radiation does not affect the methylation status
of Alu elements. In contrast, it affected the methylation status
Figure 3 | Correlation between FPG and ERK2 level based on the Pearson’s Correlation test in cataract (a), diabetic (b), and control (c) samples.
Significance of correlation coefficients (r)wassetatP<0.05.
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y = 0.021x + 1.7669
R² = 0.5055
Pearson Correlation = 0.711
P value < 0.01
-4
-2
0
2
4
6
8
10
12
14
16
50 150 250 350 450 550
ERK1 expression (Delta CT)
TAC
(a) Cataract
y = 0.0205x + 1.1724
R² = 0.6703
Pearson Correlation = 0.819
P value < 0.01
-5
0
5
10
15
20
50 150 250 350 450 550
ERK1 expresion (Delta CT)
TAC
(b) Diabetic
y = -0.0023x + 10.001
R² = 0.0068
Pearson Correlation = - 0.082
P value = 0.633
-5
0
5
10
15
20
100 200 300 400 500 600 700
ERK1 expression(delta CT)
TAC
(c) Control
Figure 4 | Correlation between TAC level and ERK1 expression based on the Pearson’s Correlation test in cataract (a), diabetic (b), and control (c)
samples. Significance of correlation coefficients (r)wassetatP<0.05.
10
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ORIGINAL ARTICLE
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y = 0.0096x -0.9173
R² = 0.5068
Pearson Correlation = 0.712
P value < 0.01
-4
-3
-2
-1
0
1
2
3
4
5
6
0 100 200 300 400 500 600 700
ERK2 expression(delta CT)
TAC
(a) Cataract
y = -0.0018x + 3.353
R² = 0.0251
Pearson Correlation = - 0.158
P value = 0.356
-2
-1
0
1
2
3
4
5
6
0 200 400 600 800
ERK2 expression(delta CT)
TAC
(c) Control
y = 0.0131x - 0.3649
R² = 0.044
Pearson Correlaon = 0.209
P value = 0.023
-2
-1
0
1
2
3
4
5
6
0 100 200 300 400
ERK2 expression(delta CT)
TAC
(b) Diabetic
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of LINE-1 elements
56
. LINE-1, but not Alu, hypomethylation
was reported in SLE
21, 24
, epidermis of psoriasis
23
,andhead
and neck cancers
57
.
Considering the factors affecting the change of the methyla-
tion level of the genome, it is not surprising that the methyla-
tion status of Alu sequences or its relationship with
demographic or clinicopathological components is different
from that of LINE-1 sequence.
The aforementioned reports indicate that there is a relation-
ship between the level of oxidative stress and genome hypo-
methylation. In the present study, there was a significant
association between TAC and the methylation level of Alu
sequences. Alu hypomethylation may activate these sequences,
which in turn, could lead to genome instability and changes in
gene expression
58
.
The mechanism underlying aberrant DNA hypomethylation
in type 2 diabetes is not yet fully understood. Recent studies
indicate the possible role of free radicals, particularly reactive
oxygen species (ROS), in this process
59
. Guanine is particularly
vulnerable to oxidative damage, leading to its conversion into
8-hydroxydeoxyguanosine, which decreases the binding affinity
of DNA methyltransferases (DNMT) for DNA and promotes
the demethylation of neighboring 5-methylcytosines
60
.Experi-
mental evidence indicates that in cells exposed to H
2
O
2
,there
is a significant decrease in the S-adenosylmethionine (SAM)
levels and a reduction in the methylation status of LINE-1
sequences, while glutathione levels increase
61
. However, in the
presence of alpha-tocopherol acetate or N-acetylcysteine, the
SAM and methylation levels return to their original state
61
.
Contrary to Alu, our study showed no significant association
between TAC and the methylation levels of LINE-1 elements.
Seddon et al. investigated the effects of H
2
O
2
on DNA methyl-
ation and showed that exposure to H
2
O
2
not only led to a
decrease in overall genome methylation (approximately 3.3%)
but also to changes in methylation patterns at specific loci. This
site-specific variation suggests that the effects of H
2
O
2
are not
uniform throughout the genome, leading to different responses
depending on the location of the DNMT1 in the DNA. Possi-
bly, H
2
O
2
may affect DNA methylation through undiscovered
mechanisms beyond direct DNMT1 inhibition that potentially
involve DNMT1 localization in the genome, leading to site-
specific methylation, especially in an asynchronous cell popula-
tion and eventually in heterogeneity of methylation profiles
after oxidative stress
62
. Also, other factors can lead to different
responses of Alu and line sequences, such as environmental
exposure
63
and shifts in circulating leukocyte subtypes due to
the body’sinflammatory response, may account for differences
in DNA methylation patterns
64
.
DespitethefactthatROScaninfluence both global and site-
specific DNA methylation patterns, these observations demon-
strate that methylation changes at LINE-1 and Alu elements
occur in a site-specific manner rather than across the entire
genome.
Reports have also revealed different associations between
clinicopathological components and Alu and LINE-1
methylation. In subjects exposed to air pollution, hypomethyla-
tion of LINE-1 was associated with higher arterial blood pres-
sure and inversely with serum levels of vascular cell adhesion
molecule-1 (VCAM-1). In contrast, Alu methylation shows no
association with arterial blood pressure, VCAM-1, and/or statin
use
65
.
Therefore, the level of DNA methylation at LINE-1 and Alu
elements and also their correlation with clinicopathological
aspects appear to be closely linked to the specific cellular con-
text and genetic background, reflecting the tissue-specificnature
and interindividual diversity of epigenetic modifications.
It was found that the expression levels of ERK1/2 genes were
significantly increased in diabetic and cataract patients com-
pared to the control group. Furthermore, there was a significant
correlation between FPG levels and ERK1 expression in indi-
viduals with diabetes and cataracts.
In line with our results, studies in diabetic rat models and
rat pancreatic β-cell cultures have shown that hyperglycemia
triggers the activation of MAPKs
66
. Furthermore, insulin has
been observed to induce the activity of the MAPK signaling
pathway under conditions of hyperglycemia
67
.Furthermore,in
diabetic conditions, ERK activity increases in adipose tissue of
both humans and rodents
68
.
Previous studies have highlighted the role of the MAPK sig-
naling pathway in diabetic complications such as nephropathy
and cardiopathy
69
.
The current study demonstrated that there is a significant
and inverse correlation between TAC levels and ERK1/2
expression in individuals with cataracts and diabetes. This cor-
relation suggests that increased expression or activity of ERK1/
2 may lead to increased generation of free radicals, particularly
ROS. Consistent with our results, it was observed that treat-
ment of the MDAMB231 cell line with the ERK inhibitor
(PD0325901) resulted in reduced ROS levels and attenuated
mitochondrial fragmentation
70
. In support of these results, it
has been shown that elevated glucose levels can activate ERK1/
2 kinase in cell culture models
71
through oxidative stress
72
.
The observed correlation may indicate that increased ROS
levels contribute to the upregulation of the ERK gene. In sup-
port of this notion, Traore et al.
73
have highlighted the essen-
tial role of ROS in the activation of ERK1/2 in ML-1 and
Figure 5 | Correlation between TAC level and ERK2 expression based on the Pearson’s Correlation test in cataract (a), diabetic (b), and control (c).
Significance of correlation coefficients (r)wassetatP<0.05.
12
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ORIGINAL ARTICLE
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y = -0.0028x + 2.6209
R² = 0.4675
Pearson Correlation = - 0.684
P value <0.01
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
50 150 250 350 450 550
Alu relative methylation(Δct)
TAC
(a) Cataract
y = -0.0044x + 3.0369
R² = 0.5506
Pearson Correlation = - 0.742
P value <0.01
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
50 250 450 650
Alu relative methylation(Δct)
TAC
(b) Diabetic
y = -2.597x + 375.83
R² = 0.0003
Pearson Correlation = - 0.016
P value = 0.927
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
80 180 280 380 480 580 680
Alu relative methylation(Δct)
TAC
(c) Control
Figure 6 | Correlation between TAC and Alu methylation level based on the Pearson’s Correlation test in cataract (a), diabetic (b), and control (c)
samples. Significance of correlation coefficients (r)wassetatP<0.05.
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y = 0.0015x + 0.9454
R² = 0.0388
Spearman's rho coefficient = 0.07
P value = 0.630
0
1
2
3
4
5
6
50 150 250 350 450 550
LINE-1 relative methylation(Δct)
TAC
(a) Cataract
y = 0.0005x + 1.2476
R² = 0.0037
Spearman's rho coefficient = 0.152
P value = 0.253
0
1
2
3
4
5
6
50 150250350450550650
LINE-1relave methylaon(Δct)
TAC
(b) Diabec
y = -0.0019x + 1.9832
R² = 0.1054
Spearman's rho coefficient = -0.236
P value = 0.165
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
50 250 450 650 850
LINE-1 relative methylation(Δct)
TAC
(c) Control
Figure 7 | Correlation between TAC and LINE-1 methylation level in cataract (a),diabetic(b),andcontrol(c)samples. Significance of correlation
coefficients (r)wassetatP<0.05.
14
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ORIGINAL ARTICLE
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y = 0.0016x + 0.1838
R² = 0.0313
Spearman's rho coefficient =0.14
P value = 0.318
0
0.1
0.2
0.3
0.4
0.5
0.6
35 45 55 65 75
Methylaon level
Age
(a) Cataract
y = -0.0017x + 0.4616
R² = 0.0139
Spearman's rho coefficient =0.158
P value = 0.273
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
30 40 50 60 70 80
methylaon level
Age
(b) Diabetic
y = -0.0002x + 0.4558
R² = 0.0003
Spearman's rho coefficient =0.009
P value = 0.95
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
30 50 70
Merthylaon level
Age
(c) Control
Figure 8 | Correlation between age and Alu methylation level in
cataract (a), diabetic (b), and control (c) samples. Significance of
correlation coefficients (r)wassetatP<0.05.
y = 0.0135x + 0.4985
R² = 0.025
Spearman's rho coefficient = 0.207
P value = 0.149
0
1
2
3
4
5
6
35 45 55 65 75 85
Merthylaon level
Age
(a) Cataract
y = -0.0108x + 1.6562
R² = 0.0189
Spearman's rho coefficient = 0.183
P value = 0.204
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
30 50 70
methylaon level
Age
(b) Diabetic
y = 0.0006x + 1.0468
R² = 9E-05
Spearman's rho coefficient = 0.113
P value = 0.469
0
0.5
1
1.5
2
2.5
3
3.5
4
30 40 50 60 70 80
methylaon level
Age
(c) Control
Figure 9 | Correlation between age and LINE-1 methylation level in
cataract (a), diabetic (b), and control (c) samples.
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THP-1 cells which is facilitated by the activation of Cγ-
phospholipase and an increase in the intracellular calcium
levels
74, 75
. Additionally, exogenous H
2
O
2
has been shown to
trigger ERK1/2 phosphorylation in ML-1 and eosinophil cells,
a process that can be attenuated by pretreating the cells with
antioxidants
76, 77
.
Therefore, it is likely that hyperglycemia-induced ERK1/2
expression promotes global hypomethylation of the genome,
particularly in the Alu and LINE-1 elements, by increasing the
production of ROS in mitochondria.
CONCLUSIONS
Our findings highlight the possible role of
hyperglycemia-induced oxidative stress in altering the pattern
of ERK1/2 gene expression as well as inducing aberrant hypo-
methylation in Alu and LINE-1 sequences. These aberrant
changes may play a contributing role in diabetic complications,
including cataracts. It is important to note that gene expression
and global genome hypomethylation are influenced by various
factors, such as genetic variants, personal and demographic
characteristics, lifestyle choices, and environmental exposures.
Considering the cross-sectional nature of the study, it is essen-
tial to evaluate the influence of these factors in a longitudinal
study across various populations until their potential contribu-
tion to the etiology of diabetes and its associated complications
is fully understood.
The impact of confounding variables, particularly lifestyle ele-
ments like dietary habits and physical activity, along with the
administration of medications that could modify oxidative
stress and gene expression, necessitates a more thorough
examination.
However, much is still to be learned about the contribution
of alternative pathways/mechanisms, such as inflammatory
cytokines, in contributing to Erk signaling, genome hypomethy-
lation, and cataract formation in diabetic patients.
ACKNOWLEDGMENTS
The authors of this article consider it necessary to thank all the
patients who contributed to this research, Ms. Asghari and Dr.
Saeed Karimi for sampling, and Ms. Sedigheh Yektamghadam
for her invaluable help and advice for data analysis and writing
the original draft.
DISCLOSURE
Authors declare any financial support or relationships that may
pose a conflict of interest.
Approval of the research protocol: Yes.
Informed consent: Yes.
Registration no. of the study/trial: IR.MAZUMS.REC.
1398.D114.
Animal studies: No.
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ORIGINAL ARTICLE
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