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\begin{document}

\title{Aberrant methylation of aging-related genes in asthma}



\author[1]{Yu Yang}%
\author[1]{Lin Yuan}%
\author[2]{Ming Yang}%
\author[1]{Xizi Du}%
\author[1]{Ling Qin}%
\author[1]{Leyuan Wang}%
\author[1]{Kai Zhou}%
\author[1]{Mengping Wu}%
\author[1]{Ruoxi He}%
\author[1]{Juntao Feng}%
\author[1]{Yang Xiang}%
\author[1]{Xiangping Qu}%
\author[1]{Huijun Liu}%
\author[1]{Xiaoqun Qin}%
\author[1]{Liu chi}%
\affil[1]{Central South University}%
\affil[2]{Newcastle University}%


\vspace{-1em}



  
  \date{May 03, 2024}


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\selectlanguage{english}
\begin{abstract}
Background: Asthma is a complex pulmonary inflammatory disease which is
common among older adults.Aging-related alterations have also been found
in structural cells and immune cells of asthma patients. Of note, DNA
methylation have been proven to be play a critical mechanism for
age-related gene expression changes However, the methylation changes of
aging-related genes in asthma patients is still obscure. Methods: First,
changes in DNAm and gene expression were detected with multiple targeted
bisulfite enrichment sequencing and qPCR in peripheral blood of 51
healthy controls and 55 asthmatic patients. Secondly, the correlation
between the DNAm levels of specific altered CpG sites and the pulmonary
function indicators of asthma patients was evaluated. Lastly, ROC curve
and PCA were used to identify the feasibility of the candidate CpG sites
as biomarkers for asthma. Results: Compared with HCs, there was a
differential mRNA expression for 9 aging-related gene in peripheral
blood of asthma patients. Besides, the methylation level of the 9
aging-related genes were also altered, and a total of 68 CpG sites were
associated with the severity of asthma. Moreover, ROC curve and PCA
analysis showed that the candidate differential methylation sites can be
used as potential biomarkers for asthma. Conclusions: In summary, this
study confirmed the differentially expressed mRNA and aberrant DNAm
level of aging-related genes in asthma patients. The differential DMSs
are associated with the clinical evaluation indicators of asthma, which
indicate the involvement of aging-related genes in the pathogenesis of
asthma and provide some new possible biomarker for asthma.%
\end{abstract}%



\sloppy


\textbf{Aberrant~methylation~of~aging-related~genes~in~asthma}

Yu Yang\textsuperscript{1,2,3}, Lin Yuan\textsuperscript{2}, Ming
Yang\textsuperscript{4}, Xizi Du\textsuperscript{2}, Ling
Qin\textsuperscript{1,3}, Leyuan Wang\textsuperscript{2}, Kai
Zhou\textsuperscript{2}, Mengping Wu\textsuperscript{2}, Ruoxi
He\textsuperscript{1,3}, Juntao Feng\textsuperscript{1,3}, Yang
Xiang\textsuperscript{2}, Xiangping Qu\textsuperscript{2}, Huijun
Liu\textsuperscript{2}, Xiaoqun Qin\textsuperscript{2}, Chi
Liu\textsuperscript{1,2,5}\emph{*}

\textsuperscript{1}Department of Respiratory Medicine, National Clinical
Research Center for Respiratory Diseases, Xiangya Hospital, Central
South University, Changsha, Hunan, China

\textsuperscript{2}Department of Physiology, School of Basic Medicine
Science, Central South University, Changsha, Hunan, China

\textsuperscript{3}Basic and Clinical Research Laboratory of Major
Respiratory Diseases, Central South University, Changsha, Hunan, China

\textsuperscript{4}Centre for Asthma and Respiratory Disease, School of
Biomedical Sciences and Pharmacy, Faculty of Health and Medicine,
University of Newcastle and Hunter Medical Research Institute,
Callaghan, New South Wales, Australia

\textsuperscript{5}Research Center of China-Africa Infectious Diseases,
Xiangya School of Medicine Central South University, Changsha, Hunan,
China

\textbf{Corresponding author:} Chi Liu, Department of Physiology,
Xiangya School of Medicine, Central South University at Changsha, Hunan,
China 410078. Tel. 86-73182355051, E-mail:\emph{liu.chi@csu.edu.cn}.

\textbf{Abstract}

\textbf{Background:}

Asthma is a complex pulmonary inflammatory disease which is common among
older adults. Aging-related alterations have also been found in
structural cells and immune cells of asthma patients. Nonetheless,
the~underlying~mechanism~by which differenced aging-related gene
contributes to asthma pathology~remains~unclear. Of note, DNA
methylation (DNAm) have been proven to be play a critical mechanism for
age-related gene expression changes However, the methylation\emph{~}
changes of aging-related genes in asthma patients is still obscure.

\textbf{Methods:}

First, changes in~DNAm~and gene expression were detected with multiple
targeted bisulfite enrichment sequencing (MethTarget) and qPCR in
peripheral blood of 51 healthy controls (HCs) and 55 asthmatic patients.
Secondly, the correlation between the DNAm levels of specific altered
CpG sites and the pulmonary function indicators of asthma patients was
evaluated. Lastly, the Receiver Operator Characteristic (ROC) curve and
Principal Component Analysis (PCA) were used to identify the feasibility
of the candidate CpG sites as biomarkers for asthma.

\textbf{Results:}

Compared with HCs, there was a differential~mRNA~expression for 9
aging-related gene in peripheral blood of asthma patients. Besides, the
methylation level of the 9 aging-related genes were also altered in
asthma patients, and a total of 68 CpG sites were associated with the
severity of asthma. Notably, 10 of the 68 CpG sites were significantly
associated with pulmonary function parameters. Moreover, ROC curve and
PCA analysis showed that the candidate differential methylation sites
(DMSs) can be used as potential biomarkers for asthma.

\textbf{Conclusions:}

In summary, this study confirmed the differentially expressed~mRNA and
aberrant DNAm level of aging-related genes in asthma patients. The
differential DMSs are associated with the clinical evaluation indicators
of asthma, which indicate the involvement of aging-related genes in the
pathogenesis of asthma and provide some new possible biomarker for
asthma.

\textbf{Keywords:} Aging-related genes, DNA methylation, Aging, Asthma,

\textbf{Introduction}

Asthma is a chronic~and complex pulmonary inflammation disease which is
characterized by aberrant immune responses to allergen, reversible
airflow obstruction, and airway hyper-responsiveness (AHR). Although
bronchodilators and inhaled/systemic corticosteroids are highly
effective in most asthma patients, approximately 5-10\% asthma patients
are still steroid-refractory which always have lower lung function and
higher mortality {[}1, 2{]}. Classical ''allergic constitution'' or
''airway inflammation'' cannot fully explain the occurrence and
development of asthma. Thus, accumulating studies are
attempted~to~further elucidate the inner pathogenesis~of asthma and
identify novel therapeutic targets.

Intriguingly, asthma is common among older adults (age over 65 years)
which is usually more severe, with little opportunities of remission
{[}3{]}. Accumulative studies have demonstrated the involvement of aging
in the parthenogenesis of chronic pulmonary diseases, including
idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary
diseases (COPD). As is known that the pathological changes in asthma
resemble COPD, such as airway remodeling, chronic inflammation and
decreased lung function {[}4, 5{]}. It is feasible to speculate the
possible involvement of aging in the development of asthma. Indeed, some
valuable evidences have implicated that aging is a vital dangerous
factor for the development of asthma {[}6{]}. Aging-related changes have
also been found in structural cells and immune cells of asthma patients.
Of particular note is that the hallmarks of aging such as telomere
attrition, epigenetic alterations, loss of proteostasis, and altered
intercellular communication have also been detected in asthma patients
{[}7{]}. Besides, aging can affects the asthma severity along with its
diagnosis and management which is significant for the treatment of
asthma {[}6{]}. The aging of different targeted cells can also
contribute to the pathobiology of asthma, including airway inflammation,
airway remodeling and decreased lung function {[}8{]}. Furthermore, it
has been confirmed that anti-aging strategies can improve pathological
processes such as airway inflammation and airway remodeling in asthma
patients {[}9{]}.

Although more and more undeniable studies have evidenced the association
between aging and asthma. It is still obscure about
the~role~of~aging~and~the~mechanism

behind~differential~expression of aging-related genes remains unknown. A
serious of recent researches have confirmed that epigenetic mechanisms
are involved in the regulation of the expression of aging-related gene
{[}10, 11{]}. Epigenetic mechanisms containing~DNAm, microRNAs
expression and histone~modifications could regulate the transcription
activities of target genes without alteration of nucleotide sequence. In
particularly, DNAm is the most deeply studied epigenetic regulation,
which have been proven to play a crucial role in the regulation of
aging-related genes {[}12{]}. Specifically, it has been verified that
cytosine methylation at the CpG site affected multiple regulatory
mechanisms of aging-related genes during transcription {[}13, 14{]} and
further participated in aging-related disease such as asthma and COPD
{[}15-17{]}. However, there is still no definitive literature on the
DNAm variations of aging-related genes in asthma patients.

Our previous study screened and evaluated the differentially mRNA
expression and altered methylation levels of 9 aging-related genes
(AREG, ATG3, E2F1, FOXO3, HDAC1, MMP2, NUF2, TGFB1and TP53) in COPD
patients {[}18{]}. It is found that DNAm was involved in regulating the
expression of 9 aging-related genes in peripheral venous blood of COPD
patients. Besides, the methylation level of certain special CpG sites
was associated with the incidence and severity of COPD {[}18{]}. In this
study, we further aim to probe the potential involvement of these
previous screened 9 aging-related genes in the parthenogenesis of
asthma. Firstly, we inspected the changes in~DNAm~and mRNA expression of
the 9 aging-related genes in peripheral venous blood of HCs and
asthmatic patients. Then, we analyzed the correlation between DMSs and
clinical indicators in asthmatic patients. Finally, we assessed the
feasibility of the candidate CpG sites as biomarkers for asthma.

\textbf{Methods}

\textbf{Subjects and data collection}

The study was approved by No. 20180308 of the Xiangya Hospital Ethics
Review Committee. From October 2018 to January 2019, 51 HCs and 55
asthma patients were chosen from the Respiratory Department and Physical
Examination Center of Xiangya Hospital, China. FEV\textsubscript{1}/FVC
ratio \textless{}0.7 and FEV\textsubscript{1}\% \textless{}70\% was
defined as the presence of asthma. The inclusive standards for the
patient group were between the age of 40 and 70 with a clear diagnosis
of asthma (according to the criteria of 2018 Global Strategy for Asthma
Management and Prevention) but without other respiratory
or~other~diseases {[}19{]}. The HCs had no differences in age and gender
without asthma or other organic mental diseases, including smoking and
non-smoking controls. Quality control methods were strictly enforced.

After obtaining the written informed consent from each subject, we
collected questionnaire information (general condition, smoking history
and other respiratory diseases), pulmonary function testing and
peripheral blood samples. For our analysis, pulmonary function
parameters were adopted including forced expiratory volume in one second
as percentage of predicted volume (FEV\textsubscript{1}\%), the
spirometric values of forced expiratory volume in one second
(FEV\textsubscript{1}), forced vital capacity (FVC), peak expiratory
force (PEF) and forced expiratory flow (FEF). Certified staff performed
all interviews and examinations. Moreover, feedback on work quality
would be regularly provided to field staff during the data collection
process, and secondary training would be conducted when necessary.

\textbf{Sample collection}

A total of 106 whole blood samples were collected from the enrolled 51
HCs and 55 asthma patients, respectively. Then,collected peripheral
blood was placed into 5 ml EDTA anticoagulation tubes and transferred to
a centrifuge tube. After adding 2 volumes of erythrocyte lysate and
lysing for 5 minutes, peripheral blood cells were pelleted by
centrifugation and stored at -80.

\textbf{RNA extraction and quantitative RT-PCR}

Total mRNA was purified from peripheral blood cells using Trizol
(Invitrogen) and quantified by an ultraviolet spectrophotometer (Thermo
Fisher Scientific, USA) {[}20{]}. 1\selectlanguage{greek}μ\selectlanguage{english}g RNA was reverse transcribed into
cDNA using Reverse Transcriptase Kit (Qiagen, Netherlands) accordance to
the manufacturer's instructions {[}21{]}. Then, quantitative RT-PCR was
executed using SYBR\selectlanguage{ngerman}\textsuperscript{®} Premix Ex
Taq\textsuperscript{TM}II system (TaKaRa, Japan) with the CFX96
Touch\textsuperscript{TM}Real-Time PCR Detection System (Bio\selectlanguage{english}-Rad, USA).
1\selectlanguage{greek}μ\selectlanguage{english}l of the reverse-transcript was added to a 30 \selectlanguage{greek}μ\selectlanguage{english}l PCR mixture for 40
cycles. Each cycle included 93 for 30s and 54 for 60s. By the
comparison between the copy numbers of target gene and \selectlanguage{greek}β-\selectlanguage{english}actin, the
normalization of mRNA expression data for sample-to-sample variability
in RNA input, RNA quality and reverse transcription efficiency was
completed. Primer sequences were described in Table 1.

\textbf{DNA Extraction, Bisulfite Treatment, Methylation Array Methods}

A commercially available kit (TIANGEN Biotech, Beijing, China) was used
to extract genomic DNA from whole blood according to previous
publications {[}22{]}. Genesky Biotechnologies Inc. performed bisulfite
processing, methylation library construction, high-throughput sequencing
and quality control {[}23{]}. CpG islands located between 2K upstream of
the gene transcription start site and 1K downstream of the first exon
were selected to measure methylation level. 18 CpG islands from the 9
screened aging-related genes were selected (2 from AREG, 2 from ATG3, 1
from E2F1, 3 from FOXO3, 1 from HDAC1, 3 from MMP2, 1 from NUF2, 3 from
TGFB1 and 2 from TP53) according to our previous publications {[}18{]}.
Then, bisulfite modification of DNA sample, methylation library
construction and MethTarget were performed {[}18{]}. 856 CpG sites from
9 distinguishingly expressed aging-related genes in the methylation
assay were detected. We only selected the original data with a
sequencing quality value of Q\textgreater{}40 (basic sequencing error
rate \textless{}0.1\%), and the methylation percentage of each CpG site
was presented.

\textbf{Statistical analysis}

The characteristic data of all recruited HCs and asthma patients were
showed as Mean \selectlanguage{ngerman}± SD, \emph{p} -value \textless{} 0.05, analyzed by
unpaired T test. T test and nonparametric test (Mann-Whitney U test)
were used to analyze the mRNA expression and the methylation array of
AREG, ATG3, E2F1, FOXO3, HDAC1, MMP2, NUF2, TGFB1 and TP53. We used the
Benjamin Hochberg method to control the false discovery rate (FDR). The
selection of distinguishingly expressed CpG sites was performed by
Logistic regression analysis, with latent risk factors of age and gender
{[}24{]}. The correlation between the percentage of methylation of
candidate CpG sites and successive variables for instance
FEV\textsubscript{1}\%, FVC, FEV\textsubscript{1} and PEF was assessed
by Pearson's correlation or Spearman's correlation. ROC analysis was
obtained to elucidate the accuracy of candidate DMSs or methylation
change rate s in predicting asthma. For each candidate DMS, the optimal
cutoff value for predicting asthma and corresponding sensitivity and
specificity were defined by the maximum Youden index value (sensitivity
+ specificity-1) {[}25{]}. The methylation percentage of candidate DMSs
or the methylation status (change or not change) were used for PCA to
identify asthma. For each candidate DMS, the change in methylation
status was defined by its optimal threshold {[}26{]}. The methylation
change rate in each sample mainly referred to the probability that the
methylation status of the candidate DMSs changed. The statistical
analyses were implemented using SPSS version 22.0 (IBM Corporation,
Armonk, NY, USA). A two-tailed \emph{p} -value \textless{} 0.05 was
considered statistically significant, **** \emph{p} \textless{} 0.0001;
* \emph{p} \textless{} 0.05.

\textbf{Results}

\textbf{Differential expression of the 9 screened aging-related genes in
peripheral blood of asthma patients}

In order to detect the expression of the previous screened 9
aging-related genes in asthma patients, peripheral blood was collected
from 51 HCs and 55 asthma patients, respectively. The demographic
characteristics of the all subjects was shown in Table 2. There was no
significant difference between in age between asthma patients and HCs.
Compared with HCs, the mRNA expression of AREG, ATG3, E2F1, FOXO3,
HDAC1, MMP2, NUF2, TGFB1 and TP53 in the asthma group changed
significantly (Figure 1).

\textbf{Altered methylation levels of the 9 aging-related genes in
peripheral blood of asthma patients}

As the mRNA expression of the 9 aging-related genes altered
significantly in asthma patients, we further determined the methylation
levels of the 9 aging-related genes in asthma patients. We analyzed the
total 856 CpG sites in the CpG islands of the 9 aging-related genes. The
methylation analysis result was showed via volcano maps (Figure 2). It
is showed that the methylation levels of 68 CpG sites were related to
asthma at FDR \textless{} 5\%. The detailed information of all the
differential 68 DMSs were demonstrated in Table S1.

\textbf{Potential correlation between DMSs of aging-related genes and
clinical index of asthma}

To further assess whether the differential methylation of the 9
aging-related genes is related to the occurrence and severity of asthma,
we detected the correlation between the differential 68 DMSs in
aging-related genes and the lung function indicators of asthma patients.
The results demonstrated that there were 10 DMSs significantly were
associated\emph{~} with lung function. The maximum correlation
coefficient for each DMSs was presented in Figure 3. The remaining
correlation analysis data was showed in Figure S1. For these 10 DMSs,
three DMSs (Chr4:75310649, Chr6:108883024, Chr17:7591672) were closely
related to at least three clinical indicators. In addition, other three
DMSs (Chr4:75310649, Chr20:32274088, Chr6:108882977) were related to two
clinical indicators. It has also been shown that the correlation
coefficients of the 10 DMSs were all greater than 0.38 with\emph{p}
-value \selectlanguage{english}< 0.05. It was also particularly noteworthy that Chr17:7591672
was closely related to four lung function indicators (FVC,
FEV\textsubscript{1}, PEF, FEF\textsubscript{25}), with a correlation
coefficient of 0.671 and a \emph{p} -value equal to 0.0001. These data
strongly suggested that theses differential DNAm of the specific
aging-related DMSs may influence the occurrence and severity of asthma.
The complete data for the 10 DMSs and clinical indicators were showed in
Table 3.

\textbf{Feasibility ofcandidate DMSs as biomarkers of asthma}

Since the differential 10 DMSs have been confirmed to be closely
associated to the clinical lung function of asthma patients, we further
evaluated their potential as biomarkers for asthma patients. First, ROC
analysis of the methylation levels of each candidate DMS was performed.
The areas under the curve (AUC) of 9 DMSs (\emph{p} -value\textless{}
5\%) were between 65.3\% and 76.3\%, and the AUC of 6 DMSs was greater
than 70\% (Figure 4A and Table 4). Besides, logistic regression was
conducted and the ROC of 9 candidate DMSs showed that the AUC of the
predicted probability of the 9 candidate DMSs was as high as 95.4\%, and
the result was statistically significantly (\emph{p} -value \textless{}
0.1\%, Figure 4B). These results indicated that the 9 candidate DMSs had
the potential value to be the biomarkers for asthma. Meanwhile, to
verify the above results, PCA analysis consisting of 9 candidate DMSs
was executed. The result revealed that the methylation levels of the
total 9 DMSs could effectively distinguish asthma patients from HCs
(Figure 4C).

To better understand the possible value of the 9 DMSs, we further
calculate the methylation change rate of the 9 DMSs in HCs and asthma
patients, which is a description of the possibility of methylation
status alteration. Then, the status of the changed methylation or
unchanged methylation was determined using the optimal cutoff value. The
optimal cutoffs of the 9 DMSs were calculated according to the Youden
index which was presented in Table 3. The methylation change rate of HCs
and asthmatic patients were included in Figure 5. Specially, the
methylation change rate of the total 9 DMSs in HCs showed a significant
decreasing trend, whereas significantly increased methylation change
rate was observed in asthma patients (Figure 5A). The methylation change
rate of the total 9 DMSs in asthma was 33.3\% \textasciitilde{} 100\%,
and the rate in HCs was only 0 \textasciitilde{} 55.6\%. Notably, the
change rate of a single DMS in asthma patients was between 47.27\% and
89.09\%, while it was 1.96\% \textasciitilde{} 41.17\% in HCs (Figure
5B). Similarly, asthma patients had a higher rate of methylation change.
Statistical results showed that the methylation change rate of the total
9 DMSs was significantly increased in asthma patients (\emph{p} -value
\textless{} 0.1\%, Figure 6A). In addition, ROC analysis was implemented
according to the methylation change rate of the 9 DMSs in all samples
(Figure 6B) and there was a higher AUC compared to previous method
(AUC=0.98). Moreover, the PCA analysis results also indicated that the
methylation change rate of 9 DMSs could better distinguish asthma
patients from HCs (Figure 6C).

\textbf{Discussion}

Asthma is a common chronic pulmonary disease, and the\emph{~}
incidence\emph{~} of asthma\emph{~} has\emph{~} increased\emph{~}
in~the~last~few decades {[}27{]}. With the increased\emph{~}
incidence\emph{~} of asthma, new~preventive~strategies~and~therapies~for
asthma are urgently needed to further reduce the\emph{~}
morbidity\emph{~} and\emph{~} mortality of asthma\emph{.} Of particular
note is the potential causal role of aging in~the~asthma~pathogenesis
{[}28, 29{]}. Several relevant~studies~have identified the altered
expression of aging-related genes (such as TP53 and FOXO3) in
respiratory diseases {[}30, 31{]}. The polymorphism of transcription
factor FOXO3 was confirmed to regulate the overactivation of mast cells,
down-regulation of anti-inflammatory factors and production of cytokines
during the pathogenesis of COPD and asthma {[}32{]}. FOXO3 deficiency
has been confirmed to play an important role in regulating lung
inflammation of COPD/emphysema, which has emerged as a new approach to
address the development of pulmonary inflammatory diseases {[}33{]}.
Similarly, TP53 has been implicated in~COPD\emph{~} pathogens is by
mediating the senescence of multiple lung cells, and the overexpression
of TP53 also could promote the progression of emphysema in COPD patients
{[}31, 34{]}.

Not only that, as a stable epigenetic marker, aging-related CpG sites
were either hypo-~or~hyper-methylated in COPD and other aging-related
diseases {[}35, 36{]}. Our previous research identified that DNAm was
involved in regulating the expression of 9 aging-related genes in
peripheral venous blood of COPD patients {[}18{]}. As asthma and COPD
have similar even overlapping clinical phenotypes in chronic
inflammation and decreased lung function. In this study, we further
explored the methylation change of the previous screened aging-related
genes in peripheral venous blood of asthma patients. Indeed,
the~association~between these screened 9 aging-related genes and asthma
have been extensively studied by previous literatures {[}37-44{]}. AREG,
E2F1, FOXO3,

HDAC1, MMP2, TGFB1 and TP53 have been confirmed as crucial signaling
molecules in asthma {[}30, 45-51{]}. Although ATG3 is a key central
regulator~in~autophagy~induction during aging {[}52{]}, and NUF2 is
closely related closely~associated with lung~cell~senescence {[}53{]},
their specific role in asthma has rarely been studied. The differential
expression of ATG3, FOXO3, NUF2 and TP53 in asthma patients were also
aligned with former studies {[}30, 53-55{]}. In addition, excessive
secretion of AREG in the airway after acute asthma attack promote airway
remodeling {[}51{]}. However, AREG is downregulated in peripheral blood
of elderly asthma patients, which may be attributed to the
different\emph{~} disease\emph{~} stages. It is particularly worth
noting that the decreased expression of E2F1 in asthma patients is
consistent with what we have previously observed in COPD patients
{[}18{]}, which is different from that in lung cancer patients {[}55{]}.
One possible reason is the specificity of the sample tissue and
pathogenic genes in\emph{~} different\emph{~} diseases. MMP2, as a
member of the matrix metalloproteinase family, shows an increasing trend
in the acute and chronic phases of lung disease. Our results observed
the increased expression of MMP2 in asthma patients which is consistent
with previous literatures {[}56{]}.

Additionally, we identified the methylation status of the 9
aging-related genes in asthma patients. Most DMSs of asthma patients
were hypermethylated, which was consistent with the differential
expression of mRNA, indicating that DNA~methylation regulating gene
expression is related to aging. Moreover, except for ATG3, HDAC1, and
TGFB1, correlation analysis showed that the expression of the
aging-related genes in peripheral blood of asthma patients was
associated with pulmonary function parameters (FEV\textsubscript{1}\%,
FEV\textsubscript{1}, FVC, PEF, FEF\textsubscript{75},
FEF\textsubscript{50}, FEF\textsubscript{25}). It is known that TGFB1
was a key cytokine that directs airway remodeling {[}57{]} and HDAC1
played a critical role in the pathogenesis of asthma {[}58{]}. This
partial difference may be due to the~presence~of~single nucleotide
polymorphism in asthma {[}59{]}. Chr16:55514392 located in the promoter
region has a regulatory effect on gene expression, which is inversely
associated with lung function index (FVC) {[}60{]}. Interestingly,
Chr16:55514437 is also located at the transcription initiation site, but
the~specific molecular~mechanism~which regulate gene expression still
needs further study {[}60{]}. Furthermore, there were 9 asthma-related
CpG sites on the CpG islands of the differential aging-related genes.
The ROC curve and PCA analysis of methylation level showed that all the
9 DMSs could be used as potential biomarkers to distinguish asthma from
HCs. Most notably, the methylation rate of either single DMS or total 9
DMSs in asthma patients were significantly higher than that of HCs. As
population size and ethnicity may influence~the methylation level, we
assumed that a~methylation~marker

holds promise for~better~biomarker of asthma. Our analysis of the 9 DMSs
methylation mutation rate also produced a better ROC specificity and
sensitivity, suggesting that the~combinatorial~DMSs had a great
potential to predict asthma. BALF (IL-25 and IL-33, etc.), induced
sputum (eosinophils, Th2 cells, etc.) and airway remodeling could all be
used as an useful indicators for asthma diagnosis {[}61, 62{]}. However,
the detect of DNAm in peripheral blood has greater advantage of
widespread access to samples and simple operation. Not only that, DNAm
is an important cause of asthma exacerbation, the specific role of
allergens and environmental exposure on the epigenetic modification
during the exacerbation of asthma also deserved more attention {[}63{]}.

Although our study provides potential value for diagnosis and treatment
of asthma assessment, there are also some limitations. Firstly, asthma
can be divided into different phenotypes which may have differential
epigenetic modification. Besides, our previous work is not comprehensive
enough to screen all the aging-related genes. Moreover, the sample size
is relatively small.

\textbf{Conclusions}

In a word, this study demonstrated that DNAm may regulate the
differential mRNA expression of aging-related genes in the peripheral
blood of asthma patients. Besides, the specific DMSs in aging-related
genes has~strongly associated n with pulmonary function index of asthma
patients. These results shed~new~light on DNAm that may be involved in
regulating aging-related genes in asthma, which may also provide
potential candidate biomarkers for the early diagnosis of asthma.

\textbf{Acknowledgments}

We appreciate the Key laboratory of Basic and Clinical Respiratory
Diseases in

Hunan Province for offering the equipment and experiment condition.

\textbf{Competing of interests}

The authors declare that they have no competing interests.

\textbf{Authors' contributions}

YY: carried out the experiments, analyzed and interpreted the data and
drafted the manuscript. YY, YL and WM, YM: collected clinical samples.
WL, DX, QL, ZY and WS: performed the experiments and statistical
analysis. XY, QX, LH, QX and LC: analyzed and interpreted the data,
provided the project funding and revised the manuscript. LC: analyzed
and interpreted the data, revised the manuscript and finally approved
the version of the manuscript for publication. All authors provided
critical feedback and helped shape the research, analysis and
manuscript.

\textbf{Ethics approval and consent to participate}

This study was approved by Document No. 20180308 of the Xiangya Hospital
Ethics

Review Committee. All necessary informed consents in writing were
obtained from

all patients for permission to use their clinical information and
samples for analysis.

\textbf{Availability of data and materials}

Not applicable.

\textbf{Funding information}

This work was funded by grants \#82070034, \#81970033, \#31900424,
\#81670002 and \#31671188 from the NSFC; grants \#2020JJ4776,
\#2020JJ4688, \#2019JJ50760 from the Hunan Natural Science Foundation;
grant \#20K142 from open Foundation of Hunan College Innovation Program
and grants \#2019zzts327, \#2019zzts1008 from the Fundamental Research
Funds for the Central Universities of Central South University.

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\textbf{Figure Legends}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} The mRNA levels of aging-related genes in HCs and
asthma patients. (A-I) The mRNA expression of AREG, ATG3, E2F1, FOXO3,
HDAC1, MMP2, NUF2, TGFB1, and TP53 in HCs and asthma patients. ****
\emph{p} \textless{} 0.0001.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Volcano plot of the differential methylation CpG
sites between HCs and asthma patients. The up-regulated sites were
presented as green dots and down-regulated were presented as red dots.
*\emph{p} <0.05 sites were presented above the dotted line.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} The correlation between DMSs and clinical parameters
of asthma patients. (A-B) The methylation level of Chr4:75310649 and
Chr4:75310649 were positively correlated with FEF\textsubscript{50}. (C)
The methylation level of Chr20:32274088 was positively correlated with
PEF. (D-G). The methylation level of Chr20:32274358, Chr16:55514392,
Chr16:55514437 and Chr17:7591672 were positively correlated with FVC.
(H-J) The methylation levels of Chr6:108883024, Chr1:163291825 and
Chr6:108882977 was negative correlated with FEV\textsubscript{1}\%.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} The accuracy of the methylation level of the 9 DMSs
in distinguishing asthma patients from HCs. (A) ROC curve analysis of
differential CpG sites Chr4:75310649, Chr4:75310649, Chr20:32274088,
Chr20:32274358, Chr6:108882977, Chr16:55514392, Chr16:55514437,
Chr1:163291825 and Chr17:7591672, respectively. (B) The ROC curve of the
predicted probability of the 9 DMSs. (C) A PCA plot consisting of the
methylation levels of the 9 DMSs in HCs and asthma patients.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Methylation change rate for asthma patients. (A) The
methylation change rate of the 9 DMSs in asthma patients and HCs is
represented by pie chart, and the dark shades indicates the percentage
of the methylation change rate. (B) Difference in methylation rate of
single DMS in HCs and asthma patients.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} The accuracy of the 9 DMSs' methylation change rate
in distinguishing asthma patients from HCs. (A) Statistical analysis of
the methylation change rate of the 9 DMSs in HCs and asthma patients.
(B) ROC curve analysis of the methylation change rate in the 9 DMSs. (C)
A PCA plot consisting of methylation change rate of the 9 DMSs in HCs
and asthma patients.

\textbf{Table 1.} Primer sequence of aging-related genes for qPCR.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Gene & Primer &\tabularnewline
\midrule
\endhead
AREG & forward & TGTCGCTCTTGATACTCGGC\tabularnewline
& reverse & AGGCATTTCACTCACAGGGG\tabularnewline
ATG3 & forward & GTGTTCAGTTCACCCATGCAG\tabularnewline
& reverse & TTAACAGCCATTTTGCCACTAATCT\tabularnewline
E2F1 & forward & CATCCCAGGAGGTCACTTCTG\tabularnewline
& reverse & GACAACAGCGGTTCTTGCTC\tabularnewline
FOXO3 & forward & CGGACAAACGGCTCACTCT\tabularnewline
& reverse & GGACCCGCATGAATCGACTAT\tabularnewline
HDAC1 & forward & TTTTTGGGTYGGAYGTTGAG\tabularnewline
& reverse & CCCTCRCAACCTCCTCTCC\tabularnewline
MMP2 & forward & TGGCACCCATTTACACCTAC\tabularnewline
& reverse & CCTCGTATACCGCATCAATC\tabularnewline
NUF2 & forward & TGTTAAGCAATACAAACGCACAG\tabularnewline
& reverse & TGCCTTTTCAATACCGTCGTG\tabularnewline
TGFB1 & forward & CGACTCGCCAGAGTGGTTAT\tabularnewline
& reverse & GCTAAGGCGAAAGCCCTCAA\tabularnewline
TP53 & forward & AAGTCTGTGACTTGCACGTACTCC\tabularnewline
& reverse & GTCATGTGCTGTGACTGCTTGTAG\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}actin & forward & TTCCAGCCTTCCTTCCTGGG\tabularnewline
& reverse & TTGCGCTCAGGAGGAGCAAT\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Demographic characteristics of asthma patients and
HCs.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
& Control & Asthma\tabularnewline
\midrule
\endhead
Number~of~subjects & 51 & 55\tabularnewline
Age & 53.83\selectlanguage{ngerman}±6.84 & 46.72\selectlanguage{ngerman}±10.41\tabularnewline
Gender (f/m) & 41/10 & 46/9\tabularnewline
FEV\textsubscript{1} & 2.82\selectlanguage{ngerman}±0.20 & 1.76\selectlanguage{ngerman}±0.62*\tabularnewline
FEV\textsubscript{1}~\% predicted & 0.92\selectlanguage{ngerman}±0.25 &
0.70\selectlanguage{ngerman}±0.24*\tabularnewline
FVC & 4.02\selectlanguage{ngerman}±0.65 & 2.84\selectlanguage{ngerman}±0.85*\tabularnewline
FEV\textsubscript{1}/FVC & 0.83\selectlanguage{ngerman}±0.03 & 0.65\selectlanguage{ngerman}±0.16*\tabularnewline
PEF & 8.34\selectlanguage{ngerman}±0.92 & 4.65\selectlanguage{ngerman}±0.85*\tabularnewline
FEF\textsubscript{75} & 0.86\selectlanguage{ngerman}±0.35 & 0.47\selectlanguage{ngerman}±0.22*\tabularnewline
FEF\textsubscript{50} & 0.83\selectlanguage{ngerman}±0.34 & 0.37\selectlanguage{ngerman}±0.16*\tabularnewline
FEF\textsubscript{25} & 0.72\selectlanguage{ngerman}±0.22 & 0.23\selectlanguage{ngerman}±0.18*\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

Data are presented as Mean ± SD. *\emph{p} -value \textless{} 0.05,
asthma patients VS controls (Unpaired t test).

\textbf{Table 3.} Correlation analysis between DNA methylation levels
and clinical parameters in asthma patients.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
CpG site & Gene & \emph{p}-value & \emph{p}-value & \emph{p}-value &
\emph{p}-value & \emph{p}-value & \emph{p}-value & \emph{p}-value &
\emph{p}-value\tabularnewline
\midrule
\endhead
& & FEV\textsubscript{1} & FEV\textsubscript{1}\% &
FEV\textsubscript{1}/FVC & PEF & FVC & FEF\textsubscript{75} &
FEF\textsubscript{50} & FEF\textsubscript{25}\tabularnewline
\textbf{Chr4:75310649} & AREG & 0.309 & 0.105 & 0.093 & 0.33 & 0.933 &
0.025* & 0.019* & 0.030*\tabularnewline
\textbf{Chr4:75310649} & AREG & 0.465 & 0.115 & 0.135 & 0.512 & 0.687 &
0.044* & 0.037* & 0.079\tabularnewline
\textbf{Chr20:32274088} & E2F1 & 0.035* & 0.233 & 0.223 & 0.022* & 0.051
& 0.05 & 0.05 & 0.068\tabularnewline
\textbf{Chr20:32274358} & E2F1 & 0.113 & 0.059 & 0.968 & 0.182 & 0.033*
& 0.306 & 0.543 & 0.641\tabularnewline
\textbf{Chr6:108883024} & FOXO3 & 0.044* & 0.032* & 0.063 & 0.038* &
0.238 & 0.758 & 0.195 & 0.05\tabularnewline
\textbf{Chr6:108882977} & FOXO3 & 0.063 & 0.011* & 0.055 & 0.048* &
0.366 & 0.949 & 0.147 & 0.051\tabularnewline
\textbf{Chr16:55514392} & MMP2 & 0.064 & 0.243 & 0.424 & 0.104 & 0.036*
& 0.932 & 0.365 & 0.223\tabularnewline
\textbf{Chr16:55514437} & MMP2 & 0.151 & 0.198 & 0.75 & 0.102 & 0.025* &
0.343 & 0.489 & 0.246\tabularnewline
\textbf{Chr1:163291825} & NUF2 & 0.508 & 0.038* & 0.157 & 0.202 & 0.793
& 0.106 & 0.278 & 0.366\tabularnewline
\textbf{Chr17:7591672} & TP53 & 0.001* & 0.113 & 0.575 & 0.004* & 0.000*
& 0.758 & 0.171 & 0.019*\tabularnewline
\bottomrule
\end{longtable}

A *\emph{p} -value \textless{} 0.05 was considered statistically
significant.

\textbf{Table 4.} The top 10 differentially methylated sites of the
differential aging-related genes associated with asthma.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
CpG site & Gene & AUC & \emph{p}-value & Optimal diagnostic threshold &
Sensitivity & Specificity\tabularnewline
\midrule
\endhead
\textbf{Chr4:75310649} & AREG & 0.716 & 0.009* & 0.086 & 0.724 &
0.81\tabularnewline
\textbf{Chr4:75310649} & AREG & 0.691 & 0.022* & 0.019 & 0.724 &
0.762\tabularnewline
\textbf{Chr20:32274088} & E2F1 & 0.717 & 0.009* & 0.009 & 0.517 &
0.857\tabularnewline
\textbf{Chr20:32274358} & E2F1 & 0.746 & 0.022* & 0.043 & 1 &
0.533\tabularnewline
\textbf{Chr6:108883024} & FOXO3 & 0.653 & 0.066 & 0.166 & 0.909 &
0.667\tabularnewline
\textbf{Chr6:108882977} & FOXO3 & 0.671 & 0.040* & 0.263 & 0.69 &
0.714\tabularnewline
\textbf{Chr16:55514392} & MMP2 & 0.763 & 0.038* & 0.038 & 0.69 &
0.614\tabularnewline
\textbf{Chr16:55514437} & MMP2 & 0.688 & 0.024* & 0.017 & 0.414 &
1\tabularnewline
\textbf{Chr1:163291825} & NUF2 & 0.708 & 0.010* & 0.012 & 0.862 &
0.571\tabularnewline
\textbf{Chr17:7591672} & TP53 & 0.721 & 0.008* & 0.015 & 0.966 &
0.476\tabularnewline
\bottomrule
\end{longtable}

Statistics were done by spss 22.0, A \emph{P-} value \textless{} 0.05
was considered statistically significant.

\textbf{Additional file}

\textbf{Additional file 1:Figure S1.} Correlation analysis between
differential methylation sites and clinical parameters of asthma
patients. (A-B) The methylation level of Chr4:75310649 and Chr17:7591672
were positively correlated with PEF\textsubscript{25}. (C-D) The
methylation level of Chr4:75310649 and Chr4: 75310649 were positively
correlated with FEF\textsubscript{75}. (E-G) The methylation level of
Chr20:32274088, Chr6:108883024 and Chr17:7591672 were negatively
correlated with FEV\textsubscript{1}. (H-J) Correlation between the
methylation levels of Chr6:108883024, Chr6:108882977, Chr17:7591672 and
PEF.

\textbf{Additional file 2:Table S1.} The data of 68 CpG sites related
to DNA methylation.

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