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\begin{document}

\title{Effects of ABCB1 DNA methylation in donors on tacrolimus blood
concentrations in recipients following liver transplantation.}



\author[1]{Chengcheng Shi}%
\author[1]{Liang Yan}%
\author[1]{Jie Gao}%
\author[2]{Shitong Chen}%
\author[2]{Li-Rong Zhang}%
\affil[1]{Zhengzhou University First Affiliated Hospital}%
\affil[2]{Zhengzhou University}%


\vspace{-1em}



  
  \date{May 20, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Aims: To investigate the effects of ABCB1 DNA methylation in donors on
individual differences in tacrolimus blood concentrations following
liver transplantation. Methods: Twenty-three donor liver samples
carrying the CYP3A5*3/*3 genotype were classified into two groups based
on the initial tacrolimus concentration/dose (C0/D) ratio following
liver transplantation. ABCB1 mRNA levels in liver tissues and HepG2
cells were determined by qRT-PCR. DNA methylation status in liver
tissues and HepG2 cells was determined using Illumina 850 methylation
chip sequencing technology and pyrosequencing. 5-Aza-2dC was used to
reverse methylation in HepG2 cells. Intracellular tacrolimus
concentrations were determined by liquid mass spectrometry. Results:
Genome-wide methylation sequencing and pyrosequencing analyses showed
that the methylation levels of three ABCB1 CpG sites (cg12501229,
cg00634941, and cg05496710) were significantly different between groups
with different tacrolimus C0/D ratios. ABCB1 mRNA expression in donor
livers was found to be positively correlated with tacrolimus C0/D ratio
(r = 0.458, P \textless{} 0.05). After treatment with 5-Aza-2-Dc, the
methylation levels of the ABCB1 CpG sites in HepG2 cells significantly
decreased, and this was confirmed by pyrosequencing; there was also a
significant increase in ABCB1 transcription, and this most likely
induced a decrease in intracellular tacrolimus concentrations.
Conclusions: ABCB1 CpG site methylation affects tacrolimus metabolism in
humans by regulating ABCB1 expression. Therefore, ABCB1 DNA methylation
in donor livers might be an important epigenetic factor that affects
tacrolimus blood concentrations following liver transplantation.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

The P-glycoprotein (P-gp) transporter, which is encoded by
the\emph{ABCB1} transporter gene (also known as the \emph{MDR1} gene),
is widely distributed in intestinal epithelial cells, liver cells, and
renal proximal tubule epithelial cells\textsuperscript{1}. Previous
studies have shown that \emph{ABCB1} may affect tacrolimus absorption,
distribution, and excretion \textsuperscript{2, 3}. Thus, some studies
have focused on evaluating the relationship between \emph{ABCB1} gene
polymorphism (1236C\textgreater{}T, rs1128503, Gly412Gly;
677G\textgreater{}T/A, rs2032582, Ala893DSer/Thr; 3435C\textgreater{}T,
rs1045642, Ile1145Ile of exons 12, 21, and 26) and tacrolimus
pharmacokinetics\textsuperscript{3, 4}. However, the findings of these
studies are inconsistent as concerns both SNPs and haplotypes, and
systematic mechanistic studies to support their conclusions are still
lacking. In addition, recent studies carried out on ABCB1 have shown
that it functions mainly in prolonging drug retention time in intestinal
cells by pumping drugs into the intestinal lumen i.e. on the surface of
the gastrointestinal tract, and this also affects tacrolimus
absorption\textsuperscript{5-7}. However, the effects of \emph{ABCB1} on
tacrolimus metabolism in liver cells have not been clearly elucidated.

Although \emph{CYP450} and transporter (eg. \emph{ABCB1} ) gene
polymorphisms are important for individual variations in drug metabolism
and pharmacodynamics, they do not fully explain such individual
differences\textsuperscript{8, 9}. In a previous study {[}\textbf{in
publishing progress{]}} , we recruited 78 patients with significantly
different initial tacrolimus blood concentrations
(C\textsubscript{0}\textgreater{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L or \textless{} 5 \selectlanguage{greek}μ\selectlanguage{english}g/L)
following liver transplantation at the First Affiliated Hospital of
Zhengzhou University, and corresponding donor liver samples were
collected for the genotyping of \emph{CYP3A5} and other genes which have
been reported in the literature to be possibly related to tacrolimus
metabolism\textbf{(Tables S1 and S2)} . We found significant individual
differences in tacrolimus plasma concentrations in \emph{CYP3A5}
non-expressors (\emph{CYP3A5*3/*3} ), indicating that other key factors
may also affect tacrolimus metabolism \textbf{(Figure S1, S2)} . In
recent times, the influence of epigenetic factors on drug metabolism has
received increasing research attention; among these factors, DNA
methylation has become a new research hotspot. DNA methylation can alter
gene expression through the external regulatory pathway without altering
the primary structure of DNA, thereby affecting the metabolism of drugs
and endogenous substances\textsuperscript{10}. More specifically,
cytosine on the CpG island combines with a methyl group transferred by
DNA methyltransferase (DNMT) to produce methyl cytosine, which then
inhibits DNA transcription \textsuperscript{11}. Previous studies have
shown that DNA methylation is an important epigenetic factor that
affects CYP450 gene expression\textsuperscript{12, 13}. In a previous
study, we also found DNA methylation to play an important role in
\emph{CYP3A4}transcriptional regulation\textsuperscript{14}. In
addition, anti-tumour drugs, such as daunorubicin, activate \emph{ABCB1}
transcription by hypomethylating its promoter region, and this possibly
results in multi-drug resistance\textsuperscript{15, 16}.

Therefore, for this study, we selected the liver tissues of 23 donors
carrying the \emph{CYP3A5*3/*3} genotype and exhibiting varied initial
tacrolimus blood concentrations. DNA methylation sequencing was
performed to screen for the different methylation sites of drug
metabolism enzymes or drug transporters (such as \emph{ABCB1} ), and
then we evaluated the relationship between the different methylation
sites and the tacrolimus C\textsubscript{0}/D ratio. In addition, by
treating HepG2 cells with methylase inhibitors, we further verified
whether DNA methylation is a key epigenetic factor that affects
tacrolimus metabolism.

\textbf{MATERIALS AND METHODS}

Patients and Sample collection

All the liver transplantation recipients included in this study met the
following criteria: age \textgreater{} 18 years, postoperative survival
time [?] 3 months, normal liver function within 1 month following surgery,
and patients having received treatment with tacrolimus in combination
with steroids and mycophenolate mofelil after transplantation. The
corresponding donors met the following criteria: hepatitis or other
infectious disease free, normal liver and kidney function, and no
treatment with any drug known to interact with tacrolimus within the two
previous weeks.

Tacrolimus was administered orally at a dose of 0.05 mg/kg/d twice daily
from the first day following transplantation. Routine therapeutic drug
monitoring (TDM) for tacrolimus was initiated 48 h after the
administration of its first dose. The initial tacrolimus concentration
(C\textsubscript{0}) in whole blood was determined through an enzyme
amplification immunoassay using the EMIT 2000 tacrolimus assay kit
(Siemens Healthcare Diagnostics, DE, USA) on the day of blood
collection. The tacrolimus C\textsubscript{0}/D ratio was calculated by
dividing the tacrolimus concentration by the corresponding
weight-adjusted dose. Liver samples of the included donors were
collected during liver trimming at the time of transplantation, cut into
small pieces, and immediately stored in liquid nitrogen.

DNA methylation microarray screening

The DNAs of 15 donor livers carrying the \emph{CYP3A5*3/*3} genotype
were extracted (approximately 500 ng of DNA per sample) and sent to
Shanghai Jingzhou Genomics Technology Co., Ltd., where genome-wide DNA
methylation was assessed using the Illumina Infinium Human
Methylation850K BeadChip (Illumina Inc., USA) according to the
manufacturer's instructions. Array data (.IDAT files) were analysed
using the ChAMP function in R for the determination of methylation
levels. The methylation status of all probes was denoted as the \selectlanguage{greek}β \selectlanguage{english}value,
which is the ratio of the methylated probe intensity to the overall
probe intensity (the sum of the methylated and unmethylated probe
intensities plus the constant, \selectlanguage{greek}α, \selectlanguage{english}where \selectlanguage{greek}α \selectlanguage{english}= 100). CpG sites with
\textbar{}\selectlanguage{greek}Δβ\selectlanguage{english}\textbar{} [?] 0.20 (in test \emph{vs.} control) and
adjusted\emph{P} value [?] 0.05, were considered to be differentially
methylated sites. A CpG site was considered to be hypermethylated if its
\selectlanguage{greek}Δβ \selectlanguage{english}was [?]0.20, or hypomethylated if its \selectlanguage{greek}Δβ \selectlanguage{english}was [?] -0.20. The average \selectlanguage{greek}β
\selectlanguage{english}values for promoters and CpG islands were compared between Group1 (G1:
low tacrolimus C\textsubscript{0}/D ratio group) and Group2 (G2: high
tacrolimus C\textsubscript{0}/D ratio group). Promoters and CpG islands
with \textbar{}\selectlanguage{greek}Δβ\selectlanguage{english}\textbar{} [?] 0.20 and adjusted \emph{P} value [?] 0.05,
were retained for further analysis.

Pyrosequencing analysis

Genomic DNA (500 ng) extracted from the 23 \emph{CYP3A5*3/*3} genotype
donor livers was transformed by sodium bisulfite using the EZ DNA
Methylation Kit (Zymo Research, Orange County, California, USA) and
further purified using the Wizard DNA Clean-up System (Promega, Madison,
Wis, USA). The sequences of the primers used for the \emph{ABCB1} DNA
methylation analysis are shown in \textbf{Table 1} . Each PCR reaction
system consisted of 100 ng of DNA converted by sodium bisulfite, 100 pM
deoxyriboside triphosphate, 10 pM positive/reverse primers, and 1 unit
of Taq polymerase (Merck KGaA, Darmstadt, Germany), which resulted in a
final volume of 25 \selectlanguage{greek}μ\selectlanguage{english}L. After initial denaturation at 95 \selectlanguage{ngerman}°C for 5 min,
amplification was performed for 40 cycles at 95 °C for 30 s, 60 °C for
30 s, and 72 °C for 30 s, followed by a final elongation step at 72 °C
for 5 min. PCR products were analysed by non-denatured 6\%
polyacrylamide gel electrophoresis, following staining with ethidium
bromide.

In addition, genomic DNA was extracted from cells treated with the
methylation inhibitor, 5-Aza-2-DC, and changes in \emph{ABCB1} DNA
methylation were also evaluated by pyrosequencing.

Cell culture and treatments

The human hepatocellular carcinoma cell line, HepG2, which was purchased
from the China Centre for Type Culture Collection (Wuhan, China), and
confirmed by short tandem repeat analysis, was cultured in DMEM
(HyClone/Thermo Fisher Scientific, Beijing, China) supplemented with
10\% FBS (LONSA SCIENCE S.R.L., Montevideo, Uruguay). To determine the
optimum concentration and administration period for 5-Aza-2-DC (Selleck,
Shanghai, China) and tacrolimus (Selleck, Shanghai, China), HepG2 cells
were seeded into 12-well plates at a density of 4×10\textsuperscript{4}
cells/well and cultured for 24 h at 37 °C in a 5\% CO\textsubscript{2}
atmosphere. For treatment with 5-Aza-2-DC, the cells were exposed to
5-Aza-2-DC dissolved in DMSO (0.1\% v/v) at a series of final
concentrations (0, 0.1, 0.5, 1.0, 2.5, 5.0, 10, and 50 \selectlanguage{greek}μ\selectlanguage{english}M) for 24, 48,
and 72 h. Then, cell viability was evaluated using the CCK-8 kit
(Dojindo, Shanghai, China) according to the manufacturer's instructions,
and \emph{ABCB1} mRNA and protein expression levels were evaluated by
RT-qPCR and Western blotting, respectively. For treatment with
tacrolimus, the cells were exposed to tacrolimus dissolved in DMSO
(0.1\% v/v) at a series of final concentrations (0, 0.01, 0.1, 1.0, 10,
50, 100, and 200 \selectlanguage{greek}μ\selectlanguage{english}M) for 24, 48, and 72 h, and cell viability was
evaluated using the CCK-8 kit. Based on the results of the optimum time
determined by CCK-8 analysis, HepG2 cells were treated with 0, 40, 50,
60, 70, 80, 90, and 100 \selectlanguage{greek}μ\selectlanguage{english}M tacrolimus for 24 h, and cell viability was
evaluated using the CCK-8 kit to determine the tacrolimus optimum
concentration.

5-Aza-2-DC treatment

HepG2 cells at the logarithmic growth stage
(1.2\selectlanguage{ngerman}×10\textsuperscript{6}cells/well) were spread on 6-well plates.
After 24 h of culturing, the cells were treated with 0 \selectlanguage{greek}μ\selectlanguage{english}M (DMSO; 0.1\%
v/v) or 10 \selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC, and the 5-Aza-2-DC-containing medium was
replaced every 24 h. After treatment with 5-Aza-2-DC for 72 h, the
medium was discarded and replaced with a tacrolimus-containing medium
(60 \selectlanguage{greek}μ\selectlanguage{english}M), and the cells were cultured for another 48 h. After 48 h, the
medium was replaced with tacrolimus-free DMEM and the cells were further
cultured. Samples of the medium were taken at 0, 4, 6, and 12 h
following its replacement. Next, the cells were digested with trypsin
for 2 min, and then digestion was terminated by adding more medium. The
cells were centrifuged at 300\selectlanguage{ngerman}×\emph{g} for 3 min and washed thrice with
PBS. The supernatant of cell culture and cells were stored at \selectlanguage{english}-80 degC for
further evaluation.

Total RNA extraction and RT-qPCR analysis

Total RNA was extracted using the TRIzol Reagent (CoWin Biosciences,
Jiangsu, China) according to the manufacturer's instructions. The
extracted RNA was reverse-transcribed into cDNA using Super Script III
Reverse Transcriptase (Vazyme Biotech Co., Ltd., Nanjing, China). ABCB1
mRNA expression was evaluated through a relative quantitative method.
The formula, 2\selectlanguage{english}\textsuperscript{-^^}CT, was used to calculate the
quantity of DNA amplified. The sequencing primers used for
amplification, which were provided by Tsingke Biotechnology Co., Ltd.,
were;

ABCB1-F: 5\selectlanguage{english}'-TTGCTGCTTACATTCAGGTTTCA-3\selectlanguage{english}',

ABCB1-R: 5\selectlanguage{english}'-AGCCTATCTCCTGTCGCATTA-3\selectlanguage{english}',

GAPDH-F: 5\selectlanguage{english}'-GCACCGTCAAGGCTGAGAAC-3\selectlanguage{english}',

GAPDH-R: 5\selectlanguage{english}'-TGGTGAAGACGCCAGTGGA-3\selectlanguage{english}'.

Western blot analysis

Lysates of HepG2 cells (which were treated with 5-Aza-2-DC and
tacrolimus) were run on 8\% sodium dodecyl sulphate-polyacrylamide gels,
transferred to polyvinylidene difluoride membranes, and incubated with P
glycoprotein (Abcam Inc. Cambridge, MA). Antibody binding was detected
using enhanced chemiluminescence ECL Plus western blotting detection
reagents (Merck Millipore Ltd. Tullagreen, Carrigtwohill, Co. Cork,
IRELAND).

Determination of tacrolimus intracellular concentrations by liquid-mass
spectrometry

The collected cells were resuspended in 100 \selectlanguage{greek}μ\selectlanguage{english}L of PBS or medium and
mixed with 50 \selectlanguage{greek}μ\selectlanguage{english}L of internal standard (ascomycin, 800 ng/ mL, dissolved
in methanol), 50 \selectlanguage{greek}μ\selectlanguage{english}L of methanol, and 500 \selectlanguage{greek}μ\selectlanguage{english}L of methyl-tert-butyl ether.
After vortexing and centrifugation (14000\selectlanguage{ngerman}×\emph{g} , 5 min, 4 °C), 450
\selectlanguage{greek}μ\selectlanguage{english}L of the organic layer was dried on a nitrogen blow-dry apparatus. The
extract was redissolved in 50 \selectlanguage{greek}μ\selectlanguage{english}L of a complex solution (2 mM ammonium
acetate and 0.1\% formic acid). After vortexing and centrifugation
(12000\selectlanguage{ngerman}×\emph{g} , 10 min, 4 °C), the supernatant (45 \selectlanguage{greek}μ\selectlanguage{english}L) was placed in a
liquid injection flask. The mobile phase for UHPLC-MS/MS detection was
{[}acetonitrile (containing 0.1\% formic acid): 2 mmol/L ammonium
acetate (containing 0.1\% formic acid) = 9:1{]}, and the column
temperature was 55 \selectlanguage{ngerman}°C. The specific product ions were m/z 821.5 and m/z
809.5 for tacrolimus and the internal standard, respectively. Ionisation
was carried out in positive ion mode with a capillary voltage of 3.5 kV,
a cone voltage of 22/29 (tacrolimus/internal standard), an ion source
temperature of 120 °C, a desolvation temperature of 350 °C, a nitrogen
flow rate of 600 L/h, a collision pressure of 5×10\textsuperscript{-3}
bar, and a collision energy of 17/21 (tacrolimus/internal standard).

Statistical analysis

The SPSS software (Armonk, New York corporation, USA) was used for data
analysis, and GraphPad Prism (version 6.0, San Diego, California, USA)
was used to plot graphs. All data are expressed as the mean ± SD. The
independent sample t-test was used to compare and analyse data between
the two groups, with homogenous variance of the bivariate normal
distribution. One-way analysis of variance was used to analyse the
differences between the two groups. Normally distributed bivariate data
were analysed using the Pearson correlation test, while non-normally
distributed bivariate data were analysed using the Spearman correlation
test. \emph{P} values \textless{} 0.05 were considered statistically
significant.

\textbf{RESULTS}

Whole genome methylation microarray sequencing of CYP3A5*3/*3 genotype
donor livers

Through principal component analysis, approximately 800,000 methylation
sites were observed in the two-dimensional diagram after probe filtering
and normalisation (\textbf{Figure 1A} ). Based on the
methylation\selectlanguage{greek}\emph{β} \selectlanguage{english}value between the 5 kb upstream and downstream
transcription start sites (TSS), significant differences in methylation
levels were observed between groups G1 and G2 (\textbf{Figure 1B} ). In
addition, heat and scatter maps showed that there was a large number of
methylation sites, with different levels of methylation, between groups
G1 and G2 (\textbf{Figure 1C and D} ).

Methylation levels and mRNA expression of ABCB1 in relation to
tacrolimus blood concentration

Through methylation chip screening, the methylation levels of three
\emph{ABCB1} sites (cg12501229, cg00634941, and cg05496710) located on
chromosome 7 were found to be significantly different between the high
and low tacrolimus blood concentration groups (\textbf{Figure 2A} ).
Using the pyrophosphorylation assay, the methylation levels of these
three sites were further evaluated in the 23 donor liver specimens
carrying the \emph{CYP3A5*3/*3} genotype. We found that the methylation
levels of these sites in the high C\textsubscript{0}/D group were all
significantly lower than those in the low C\textsubscript{0}/D group
(\textbf{Figure 2B-D} ).

Furthermore, \emph{ABCB1} mRNA expression levels were determined by
RT-qPCR, and correlation analyses revealed that \emph{ABCB1} mRNA
expression in donor livers was positively correlated with the tacrolimus
C\textsubscript{0}/D ratio (r = 0.458, \emph{P} \textless{} 0.05)
(\textbf{Figure 3A} ). An \emph{ABCB1} mRNA expression level of 0.15 was
used as the cut-off value to analyse the tacrolimus C\textsubscript{0}/D
ratio and differences between the low-and high-expression groups
(\textbf{Figure 3B and C} ). Our results showed that the greater
proportion of patients with high blood tacrolimus concentrations were
found in the high \emph{ABCB1} expression group.

Effects of 5-Aza-2-DC on ABCB1 expression in HepG2 cells

First, the optimal concentration and administration time for 5-Aza-2-DC
were determined (\textbf{Figure 4A-C} ). Then, based on findings in
literature, 10 \selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC was used to investigate the effects
of\emph{ABCB1} methylation status on its expression in HepG2 cells. We
found that after treatment with 10 \selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC for 24, 48 and 72
h,\emph{ABCB1} mRNA and protein expression levels significantly
increased at 72 h (\textbf{Figure 4D-F} ). Therefore, 10 \selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC
and 72 h were selected as the optimal concentration and treatment
duration, respectively, for the determination of the effects of
5-Aza-2-DC on the methylation levels of the three \emph{ABCB1} CpG sites
(cg12501229, cg00634941, and cg05496710).

Effects of 5-Aza-2-DC on ABCB1 methylation levels and tacrolimus
metabolism in HepG2 cells

A pyrophosphorylation assay was performed to investigate the effects of
5-Aza-2-DC on \emph{ABCB1} methylation. As shown in \textbf{Figure 5} ,
we found that the methylation levels of the \emph{ABCB1} sites in the
5-Aza-2-DC-treated group (10 \selectlanguage{greek}μ\selectlanguage{english}M) were significantly lower
(\emph{P}\textless{} 0.001) than those in the untreated group (0 \selectlanguage{greek}μ\selectlanguage{english}M). To
determine the effects of 5-Aza-2-DC on tacrolimus metabolism,
intracellular tacrolimus concentrations were determined by UHPLC-MS/MS.
We found the tacrolimus contents of 5-Aza-2-DC-treated cells to be
significantly lower than those of untreated cells at 0, 4, and 6 h
following the removal of tacrolimus-containing medium
(\emph{P}\textless{} 0.05). At 12 h, although there was a slight
increase in intracellular tacrolimus concentrations, its levels in the
5-Aza-2-DC-treated group were still significantly lower than those in
the untreated group (0 \selectlanguage{greek}μ\selectlanguage{english}M), and this may be due to its intracellular
metabolism in a dynamic equilibrium scenario (\textbf{Figure 6} ).

\textbf{DISCUSSION}

Several studies have reported that individual differences in drug
response cannot be fully explained by polymorphisms in genes encoding
drug-metabolizing enzymes or transporters\textsuperscript{17, 18}.
Recently, epigenetic modifications, which regulate the expression of
several enzymes and transporters involved in drug metabolism, have been
recognised as important factors that affect individual differences in
clinical drug response\textsuperscript{19}. DNA methylation affects the
expression of\emph{CYP450} (\emph{CYP1A1} , \emph{CYP1A2} ,
\emph{CYP1B1} ,\emph{CYP2C19} , \emph{CYP2D6} , \emph{CYP2E1} , and
\emph{CYP2W1} ), thus leading to significant individual differences in
enzyme expression\textsuperscript{20-23}. In addition, DNA methylation
regulates the expression of \emph{ABCG2} and \emph{ABCB1} , which play a
crucial role in determining the success or failure of cancer
chemotherapy by mediating multi-drug resistance and individual
differences in drug transport\textsuperscript{24, 25}. As most studies
on the methylation of genes encoding drug transporters have been carried
out in the field of oncology, we investigated, for the first time,
whether \emph{ABCB1} DNA methylation in donor livers affects tacrolimus
plasma concentrations in liver transplant recipients by regulating its
expression.

In this study, we analysed 15 donor liver samples carrying
the\emph{CYP3A5*3/*} 3 genotype using DNA methylation microarray
technology, and we found \emph{ABCB1} methylation levels to be
correlated with tacrolimus serum concentrations in liver transplantation
patients. Based on the findings of previous studies carried out on
\emph{ABCB1}methylation and tacrolimus metabolism,\textsuperscript{2,
26, 27} we speculated that \emph{ABCB1} DNA methylation might be another
key factor that affects tacrolimus metabolism by regulating
\emph{ABCB1}expression.

Previous studies have demonstrated that there exists no correlation
between the frequency of \emph{ABCB1} gene polymorphisms and tacrolimus
plasma concentrations following renal
transplantation,\textsuperscript{4, 28, 29} and this is consistent with
the findings of one of our previous studies {[}\textbf{in publishing
progress{]}} . However, our studies found that there exist significant
individual differences in tacrolimus plasma concentrations in liver
transplant recipients who receive donor livers with
the\emph{CYP3A5*3/*3} genotype; thus, for the first time, we evaluated
the methylation status of 23 liver samples carrying the
\emph{CYP3A5*3/*3}genotype using a methylation microarray assay
validated by pyrosequencing. Our findings showed that DNA methylation
levels at three\emph{ABCB1} CpG sites (cg12501229, cg00634941, and
cg05496710) in donor livers were significantly different between the
high and low tacrolimus C\textsubscript{0}/D ratio groups following
liver transplantation. In addition, in consonance with the findings of
previous studies,\textsuperscript{30-32} \emph{ABCB1} mRNA levels in
donor livers were found to be negatively correlated with its methylation
levels.

To the best of our knowledge, no studies have been carried out on the
effects of \emph{ABCB1} methylation, especially of its three CpG sites
(cg12501229, cg00634941, and cg05496710), on tacrolimus metabolism.
Therefore, in this study, the effects of \emph{ABCB1} methylation on
tacrolimus metabolism were first evaluated using the methylation
inhibitor, 5-Aza-2-DC. It was shown that in HepG2 cells, \emph{ABCB1}
methylation levels at its three methylation sites significantly
decreased following treatment with 5-Aza-2-DC. Moreover, tacrolimus
intracellular concentrations significantly decreased following treatment
with 5-Aza-2-DC. In addition, \emph{ABCB1}mRNA and protein levels
significantly increased following treatment with 5-Aza-2-DC. For
clinical samples, the methylation levels of the cg12501229, cg00634941,
and cg05496710 sites in the high C\textsubscript{0}/D group were all
significantly lower than those in the low C\textsubscript{0}/D group,
and \emph{ABCB1} mRNA expression was found to be positively correlated
with tacrolimus C\textsubscript{0}/D ratio. These findings indicate that
a decrease in DNA methylation could result in an increase in
\emph{ABCB1} expression in donor livers, which would lead to an increase
in tacrolimus excretion from liver cells and a consequent increase in
tacrolimus plasma concentrations of the recipient.

This study had one limitation, namely, we did not construct a
methylation-specific expression plasmid to determine which of the
three\emph{ABCB1} methylation sites plays a leading role in the
regulation of gene expression; however, further studies will be
conducted on this in the future.

\textbf{CONCLUSION}

Our study demonstrated that DNA methylation of \emph{ABCB1} CpG sites
(cg12501229, cg00634941, and cg05496710) could regulate its expression
in donor livers, thus inducing individual differences in initial
tacrolimus concentrations following liver transplantation.

\textbf{ACKNOWLEDGEMENT}

We would like to thank Key Laboratory of Hepatobiliary and Pancreatic
Surgery and Digestive Organ Transplantation of Henan Province for donor
liver samples and data collection. And we would like to thank Wiley
Editing Services~(https://editingservices.wiley.cn/) for English
language editing. This work was supported by the grants from the
National Natural Science Foundation of China (81773815; 82073931) and
the special fund for Health Commission of Henan Province (SB201902017).

\textbf{COMPETING INTERESTS}

There are no competing interests to declare.

\textbf{DATA AVAILABILITY STATEMENT}

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

\textbf{ETHICS STATEMENT}

This study was approved by the Ethics Committee of the First Affiliated
Hospital of Zhengzhou University (2019-KY-019). Written informed consent
was obtained according to the Declaration of Helsinki.

\textbf{CONTRIBUTIONS}

CCS: conception, design, data collection, analysis of data, drafting,
and revision of manuscript. LY: conception, interpretation of results
and critical revision of manuscript. STC: analysis of data,
interpretation of results. LRZ: design and revision of manuscript.

\textbf{REFERENCES}

1. Wu X, Yin C, Ma J, et al. Polyoxypregnanes as safe, potent, and
specific ABCB1-inhibitory pro-drugs to overcome multidrug resistance in
cancer chemotherapy in vitro and in vivo. Acta Pharm Sin B
2021;11:1885-1902.

2. Mallina H, Elumalai R, S FDP, et al. Computational validation of
ABCB1 gene polymorphism and its effect on tacrolimus dose
concentration/levels in renal transplant individuals of South India.
Comput Biol Med 2021;139:104971.

3. Azam F, Khan M, Khaliq T, et al. Influence of ABCB1 gene polymorphism
on concentration to dose ratio and adverse effects of tacrolimus in
Pakistani liver transplant recipients. Pak J Med Sci 2021;37:689-694.

4. Sallustio BC, Noll BD, Hu R, et al. Tacrolimus dose, blood
concentrations and acute nephrotoxicity, but not CYP3A5/ABCB1 genetics,
are associated with allograft tacrolimus concentrations in renal
transplant recipients. Br J Clin Pharmacol 2021;87:3901-3909.

5. Qin XL, Chen X, Zhong GP, et al. Effect of Tacrolimus on the
pharmacokinetics of bioactive lignans of Wuzhi tablet (Schisandra
sphenanthera extract) and the potential roles of CYP3A and P-gp.
Phytomedicine 2014;21:766-72.

6. Li X, Zhao K, Ma N, et al. Association of ABCB1 promoter methylation
with aspirin exposure, platelet function, and clinical outcomes in
Chinese intracranial artery stenosis patients. Eur J Clin Pharmacol
2017;73:1261-1269.

7. Correa S, Binato R, Du Rocher B, et al. ABCB1 regulation through
LRPPRC is influenced by the methylation status of the GC -100 box in its
promoter. Epigenetics 2014;9:1172-83.

8. Chen Z, Cheng X, Zhang L, et al. The impact of IL-10 and CYP3A5 gene
polymorphisms on dose-adjusted trough blood tacrolimus concentrations in
early post-renal transplant recipients. Pharmacol Rep 2021;73:1418-1426.

9. Peng W, Lin Y, Zhang H, et al. Effect of ABCB1 3435C\textgreater{}T
Genetic Polymorphism on Pharmacokinetic Variables of Tacrolimus in Adult
Renal Transplant Recipients: A Systematic Review and Meta-analysis. Clin
Ther 2020;42:2049-2065.

10. Kronfol MM, Jahr FM, Dozmorov MG, et al. DNA methylation and histone
acetylation changes to cytochrome P450 2E1 regulation in normal aging
and impact on rates of drug metabolism in the liver. Geroscience
2020;42:819-832.

11. Chequin A, Costa LE, de Campos FF, et al. Antitumoral activity of
liraglutide, a new DNMT inhibitor in breast cancer cells in vitro and in
vivo. Chem Biol Interact 2021;349:109641.

12. He H, Wang P, Li SG, et al. {[}Effects of pargyline on histone
methylation in promoter and enhancer regions and transcription of
CYP3A4/3A7{]}. Yao Xue Xue Bao 2017;52:91-8.

13. Lkhagvadorj K, Zeng Z, Meyer KF, et al. Postnatal Smoke Exposure
Further Increases the Hepatic Nicotine Metabolism in Prenatally Smoke
Exposed Male Offspring and Is Linked with Aberrant Cyp2a5 Methylation.
Int J Mol Sci 2020;22.

14. Wang X, Wei L, Yang J, et al. DNA methylation determines the
regulation of pregnane X receptor on CYP3A4 expression. Clin Exp
Pharmacol Physiol 2021;48:250-259.

15. Onda K, Suzuki R, Tanaka S, et al. Decitabine, a DNA
methyltransferase inhibitor, reduces P-glycoprotein mRNA and protein
expressions and increases drug sensitivity in drug-resistant MOLT4 and
Jurkat cell lines. Anticancer Res 2012;32:4439-44.

16. Li S, Zhang Y, Ho SH, et al. Combination of tumour-infarction
therapy and chemotherapy via the co-delivery of doxorubicin and thrombin
encapsulated in tumour-targeted nanoparticles. Nat Biomed Eng
2020;4:732-742.

17. Liu H, Yang ZK, Li Y, et al. ABCB1 variants confer susceptibility to
primary open-angle glaucoma and predict individual differences to
latanoprost treatment. Biomed Pharmacother 2016;80:115-120.

18. Jonaitis P, Jonaitis L, Kupcinskas J. Role of Genetic Polymorphisms
of Cytochrome P450 2C19 in Pantoprazole Metabolism and
Pantoprazole-based Helicobacter pylori Eradication Regimens. Curr Drug
Metab 2020;21:830-837.

19. Habano W, Kawamura K, Iizuka N, et al. Analysis of DNA methylation
landscape reveals the roles of DNA methylation in the regulation of drug
metabolizing enzymes. Clin Epigenetics 2015;7:105.

20. Habano W, Gamo T, Sugai T, et al. CYP1B1, but not CYP1A1, is
downregulated by promoter methylation in colorectal cancers. Int J Oncol
2009;34:1085-91.

21. Nakajima M, Iwanari M, Yokoi T. Effects of histone deacetylation and
DNA methylation on the constitutive and TCDD-inducible expressions of
the human CYP1 family in MCF-7 and HeLa cells. Toxicol Lett
2003;144:247-56.

22. Ye L, Xu Y, Wang L, et al. Downregulation of CYP2E1 is associated
with poor prognosis and tumor progression of gliomas. Cancer Med 2021.

23. Luo R, Ge C, Xiao X, et al. Identification of genetic variations
associated with drug resistance in non-small cell lung cancer patients
undergoing systemic treatment. Brief Bioinform 2021;22.

24. Ohmura H, Ito M, Uchino K, et al. Methylation of drug
resistance-related genes in chemotherapy-sensitive Epstein-Barr
virus-associated gastric cancer. FEBS Open Bio 2020;10:147-157.

25. Xu L, Wang Y. Combined influence of ABCB1 genetic polymorphism and
DNA methylation on aspirin resistance in Chinese ischemic stroke
patients. Acta Neurol Belg 2021.

26. Wang J, Huang L, Gao P, et al. Diltiazem on tacrolimus exposure and
dose sparing in Chinese pediatric primary nephrotic syndrome: impact of
CYP3A4, CYP3A5, ABCB1, and SLCO1B3 polymorphisms. Eur J Clin Pharmacol
2021;77:71-77.

27. Vaclavikova R, Klajic J, Brynychova V, et al. Development of
highresolution melting analysis for ABCB1 promoter methylation: Clinical
consequences in breast and ovarian carcinoma. Oncol Rep 2019;42:763-774.

28. Wu P, Ni X, Wang M, et al. Polymorphisms in CYP3A5*3 and MDR1, and
haplotype modulate response to plasma levels of tacrolimus in Chinese
renal transplant patients. Ann Transplant 2011;16:54-60.

29. Yildirim E, Sahin G, Kaltus Z, et al. Effect of CYP3A5 and ABCB1
Gene Polymorphisms on Tacrolimus Blood Concentration in Renal Transplant
Recipients. Clin Lab 2019;65.

30. Wu LX, Zhao HB, Wen CJ, et al. Combined Influence of Genetic
Polymorphism and DNA Methylation on ABCB1 Expression and Function in
Healthy Chinese Males. Eur J Drug Metab Pharmacokinet 2017;42:627-634.

31. Wu LX, Wen CJ, Li Y, et al. Interindividual epigenetic variation in
ABCB1 promoter and its relationship with ABCB1 expression and function
in healthy Chinese subjects. Br J Clin Pharmacol 2015;80:1109-21.

32. Yang J, Zhou JS, Zhao YX, et al. ABCB1 hypomethylation is associated
with decreased antiplatelet effects of clopidogrel in Chinese ischemic
stroke patients. Pharmazie 2015;70:97-102.

\textbf{FIGURES AND TABLES}

\textbf{Table 1} Primers used for \emph{ABCB1} DNA methylation analysis\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Detected site & Segment & Primer sequence (5'-3') & Product size
(bp)\tabularnewline
cg05496710 & PCR sense & AAGTAGAATATTTAGGGGAGGTATG & 84\tabularnewline
& PCR antisense & ACCTTACATTCTCCAATTCTTTAAAA &\tabularnewline
& Sequencing & GGAGGTATGGGTTTT &\tabularnewline
cg00634941 & PCR sense & TGGATTGTTGGATTTGTAGTTTTAATAG &
102\tabularnewline
& PCR antisense & AAACTCCAATCCCCTTTACTAATA &\tabularnewline
& Sequencing & GTTTTTAGGTGATGTTGAT &\tabularnewline
cg12501229 & PCR sense & GTGAATAGTTGGTGATAATTATTTATTGTG &
99\tabularnewline
& PCR antisense & AAATAAACATCTCCTTTTTAAACATTTT &\tabularnewline
& Sequencing & TGGTGATAATTATTTATTGTGG &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1} Whole genome methylation microarray sequencing
of\emph{CYP3A5*3/*3} genotype donor liver. \textbf{(A)} Principal
Component Analysis (PCA) diagram; \textbf{(B)} Distribution of
methylation degree between the 5 kb upstream and downstream
transcription start sites (TSS); \textbf{(C)} Heat map of differential
methylation sites; \textbf{(D)} Scatter plot of different methylation
sites. G1: low tacrolimus C\textsubscript{0}/D ratio group; G2: high
tacrolimus C\textsubscript{0}/D ratio group; n = 15.

\textbf{Figure 2} The methylation sites (red vertical bars) within
the\emph{ABCB1} methylation region located on chromosome 7 \textbf{(A)}
and the methylation level at cg12501229 site \textbf{(B)} , cg00634941
site\textbf{(C)} , and cg05496710 site \textbf{(D)} of \emph{ABCB1} gene
in donor livers carried with \emph{CYP3A5*3/*3} genotype and different
tacrolimus C\textsubscript{0}/D ratio (High: n=12, Low: n=11) ;
**\emph{P} \textless{}0.01.

\textbf{Figure 3} Correlation of \emph{ABCB1} mRNA expression with
tacrolimus C\textsubscript{0}/D ratio in \emph{CYP3A5*3/*3} donor livers
(n=23). \textbf{(A)} correlation analyses; \textbf{(B} , \textbf{C)}
An\emph{ABCB1} mRNA expression level of 0.15 was used as the cut-off
value to analyse the tacrolimus C\textsubscript{0}/D ratio (\textbf{B} )
and the tacrolimus C\textsubscript{0}/D ratio distribution (\textbf{C}
); *\emph{P} \textless{}0.05.

\textbf{Figure 4} Effects of 5-Aza-2-DC on cell viability,
\emph{ABCB1}expression in HepG2 cells. (\textbf{A-C} ) The effects of
5-Aza-2-DC on cell viability after treated at indicated concentrations
of 5-Aza-2-DC for 24h (\textbf{A} ), 48h (\textbf{B} ), and 72h
(\textbf{C} ), respectively; (\textbf{D-F} ) \emph{ABCB1} mRNA
expression (\textbf{D} ) and protein expression (\textbf{E} , \textbf{F}
) after treated with 5-Aza-2-DC (10 \selectlanguage{greek}μ\selectlanguage{english}M) for 24 h, 48 h, 72 h treatment,
respectively; *\emph{P} \textless{}0.05,**\emph{P}
\textless{}0.01,***\emph{P} \textless{}0.001\emph{vs} 0\selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC
(DMSO; 0.1\% v/v).

\textbf{Figure 5} Effects of 5-Aza-2-DC (10 \selectlanguage{greek}μ\selectlanguage{english}M) on DNA methylation at
three CpG sites located on \emph{ABCB1} promoter region in HepG2 cells;
***\emph{P} \textless{}0.001 \emph{vs} 0\selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC (DMSO; 0.1\% v/v).

\textbf{Figure 6} Changes of tacrolimus concentration in HepG2 cells
over time replaced with tacrolimus-free DMEM after treated with
5-Aza-2-DC (0 \selectlanguage{greek}μ\selectlanguage{english}M or 10 \selectlanguage{greek}μ\selectlanguage{english}M) 72h and then incubated with 60 \selectlanguage{greek}μ\selectlanguage{english}M tacrolimus
for 48 h; *\emph{P} \textless{}0.05, ***\emph{P}
\textless{}0.001\emph{vs} 0\selectlanguage{greek}μ\selectlanguage{english}M 5-Aza-2-DC (DMSO; 0.1\% v/v).\selectlanguage{english}
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