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MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020
Abstract. Intestinal surface epithelial cells (IECs) have long
been considered as an effective barrier for maintaining water
and electrolyte balance, and are involved in the mechanism of
nutrient absorption. When intestinal inammation occurs, it is
often accompanied by IEC malfunction. Berberine (BBR) is
an isoquinoline alkaloid found in numerous types of medic-
inal plants, which has been clinically used in China to treat
symptoms of gastrointestinal pathogenic bacterial infection,
especially bacteria‑induced diarrhea and inammation. In the
present study, IEC-18 rat intestinal epithelial cells were treated
with lipopolysaccharide (LPS) to establish an in vitro model of
epithelial cell inammation, and the cells were subsequently
treated with BBR in order to elucidate the anti‑inammatory
mechanism. Transcriptome data were then searched to nd
the differentially expressed genes (DEGs) compared between
two of the treatment groups (namely, the LPS and LPS+BBR
groups), and DEGs were analyzed using Gene Ontology,
Kyoto Encyclopedia of Genes and Genomes, Weighted Gene
Correlation Network Analysis and Interactive Pathways
Explorer to identify the functions and pathways enriched
with DEGs. Finally, reverse transcription-quantitative PCR
was used to verify the transcriptome data. These experiments
revealed that, comparing between the LPS and LPS+BBR
groups, the functions and pathways enriched in DEGs were
‘DNA replication’, ‘cell cycle’, ‘apoptosis’, ‘leukocyte migra-
tion’ and the ‘NF-κB and AP-1 pathways’. The results revealed
that BBR is able to restrict DNA replication, inhibit the cell
cycle and promote apoptosis. It can also inhibit the classic
inammatory pathways, such as those mediated by NF‑κB
and AP-1, and the expression of various chemokines to prevent
the migration of leukocytes. According to transcriptomic data,
BBR can exert its anti‑inammatory effects by regulating a
variety of cellular physiological activities, including cell cycle,
apoptosis, inammatory pathways and leukocyte migration.
Introduction
Inammation is the body's response to harmful stimuli and it is
usually benecial to our health. It is triggered as an automatic
defense response, but occasionally it can also be harmful to our
bodies, through attacking bodily tissues. Severe inammation
results in a series of diseases, including cancer (1), diabetes (2),
cardiovascular diseases (3) and metabolic diseases (4). Recent
evidence has suggested that the intestinal epithelium contrib-
utes to the development and perpetuation of inammation
in different types of inflammatory bowel diseases (IBDs),
including ulcerative colitis (UC) and Crohn's disease (CD) (5).
While both UC and CD share an exaggerated immune response
and some common symptoms, such as neutrophil aggregation
and plasma cell invasion, as their pathological markers, there
are differences related to their location within the gastroin-
testinal tract (6). UC is a chronic non‑specic inammation
of the colon. In severe cases, the patient will develop ulcers.
The lesions are mainly located in the colonic mucosa and
submucosa, and are distributed continuously (7). CD is a
chronic granulomatous inammation that affects all parts of
the gastrointestinal tract, especially the ileum, and presents
a segmental distribution (8). In addition to functioning as a
barrier, intestinal epithelial cells (IECs) act both as sensors
for pathogen- and damage-associated molecular patterns and
as regulators of immune cells (9,10). Therefore, in the present
study IEC-18 cells, which are a rat ileum epithelial cell line,
were used to investigate the effect of berberine (BBR) in IBD,
especially in CD.
Lipopolysaccharide (LPS), an endotoxin obtained from
Gram-negative bacteria, is able to exert its physiological
effects by interacting with toll-like receptor 4 (TLR4), a
Anti‑inammatory mechanism of berberine
on lipopolysaccharide‑induced IEC‑18 models
based on comparative transcriptomics
XIAOFAN XU, LE ZHANG, YA ZHAO, BAOYANG XU, WENXIA QIN,
YIQIN YAN, BOQI YIN, CHUYU XI and LIBAO MA
Department of Animal Nutrition and Feed Science, College of Animal Science and Technology,
Huazhong Agricultural University, Wuhan, Hubei 430070, P.R. China
Received April 28, 2020; Accepted September 24, 2020
DOI: 10.3892/mmr.2020.11602
Correspondence to: Professor Libao Ma, Department of Animal
Nutrition and Feed Science, College of Animal Science and
Technology, Huazhong Agricultural University, 1 Shizishan Street,
Hongshan, Wuhan, Hubei 430070, P.R. China
E-mail: malibao@mail.hzau.edu.cn
Key words: transcriptomics, intestinal inflammation, Gene
Ontology and Kyoto Encyclopedia of Genes and Genomes analysis,
Weighted Gene Correlation Network Analysis, cell cycle, apoptosis,
NF-κB pathway, leukocyte migration
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5164
member of the TLR family, on the cell-membrane surface of
host cells (11). The TLR family is associated with the expres-
sion of inammatory cytokines, and has an important role in
natural immunity (12). LPS has been widely used as a model of
inammation to study the anti‑inammatory inuence of drugs
or other bioactive compounds. For example, LPS was used as
the inammatory model to study the mechanism underlying
how the avonoid luteolin may prevent LPS‑induced NF‑κB
signaling and gene expression (13).
BBR is an isoquinoline alkaloid found as the major alkaloid
in numerous types of medicinal plants, including the families
Papaveraceae, Berberidaceae, Fumariaceae, Menispermaceae,
Ranunculaceae, Rutaceae and Annonaceae (14). BBR has been
used as an over-the-counter drug in the clinical treatment of
diarrhea, but modern pharmacological studies have demon-
strated that BBR has signicant antiarrhythmic, antiplatelet and
anti‑inammatory effects, has the ability to reduce cholesterol
levels and vascular smooth muscle proliferation, and can
improve insulin resistance (15). The primary anti‑inammatory
pharmacological action of BBR is to inhibit the production
and activity of inammatory cytokines (16). Since BBR has
been widely used as an anti‑inammatory drug, it was found
to be a causative agent in inactivating the NLRP3 inamma-
some in monosodium urate crystal‑induced inammation (17).
Additionally, BBR was revealed to inhibit the basal and
12-O-tetradecanoylphorbol-13-acetate (TPA)-mediated levels
of prostaglandin E2 and cyclooxygenase-2 (COX-2) expression
by inhibiting the binding of AP-1 (18). BBR also upregulated
activating transcription factor 3 (ATF-3) expression in murine
macrophages, subsequently reducing proinammatory cytokine
production via TLR signaling (19). BBR potently suppressed
the inflammatory response in macrophages by inhibiting
NF-κB signaling via sirtuin-1-dependent mechanisms (20).
Administration of BBR can notably ameliorate disease severity
and restore the mucosal barrier homeostasis of patients with
UC (21), and upregulate P-glycoprotein via activation of nuclear
factor erythroid 2-related factor 2-dependent mechanisms to
improve symptoms in patients with UC (22). BBR also inhibits
inflammatory responses as well as T helper cell (Th)1/Th7
differentiation to ameliorate 2,4,6-Trinitrobenzenesulfonic acid
solution (TNBS)-induced IBD (23). However, the transcriptome
analysis has rarely been applied to interpret the pharmacological
action of BBR. To the best of our knowledge, the present study is
the rst attempt that has been made at applying transcriptomics
to elucidate the anti‑inammatory mechanisms underlying the
effects of BBR on an LPS-induced in vivo inammation model.
The efcacy of BBR in different disease states has led to an
increased interest in its pharmacological activities. However, the
number of unrelated molecules that are targeted by BBR makes
it a complicated challenge to unravel its mechanism of action.
The mechanism underlying its anti-inflammatory activity
remains unclear, in spite of the signicant amount of relevant
data that are available.
In the present study, high-throughput RNA sequencing, as
well as functional enrichment of Gene Ontology (GO) terms,
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
analysis and Weighted Gene Correlation Network Analysis
(WGCNA), were applied to analyze differentially expressed
genes (DEGs) between LPS-induced and BBR-treated
groups. The objective of the present study was to reveal the
anti-inflammatory mechanism of BBR in an LPS-induced
IEC‑18 inammatory model at the transcriptome level. The
results obtained herein may help to further unveil the mecha-
nisms of BBR's anti‑inammatory action.
Materials and methods
Cells and drugs. Rat IECs (IEC-18 cell line) were purchased
from Hunan Fenghui Biotechnology Co., Ltd., and BBR
(PubChem CID: 2353) was purchased from Merck KGaA.
DMSO was also purchased from Merck KGaA, and Gibco®
DMEM was purchased from Thermo Fisher Scientic, Inc.
Sample treatment and collection. A total of four treatment
groups were devised for the experiments in the present study
(the control group, the LPS group, the LPS+BBR group and
the LPS+DMSO group). Cells in the control group were
cultured in normal culture medium for 12 h, and then total
RNA was collected from the cells using TRIzol® reagent
(Invitrogen; Thermo Fisher Scientic, Inc.). Cells in the LPS
group were cultured in culture medium with LPS (10 µg/ml)
(Sigma-Aldrich; Merck KGaA) for 12 h prior to collection of
the total RNA from the cells. Cells in the LPS+BBR group were
cultured in culture medium with LPS (10 µg/ml) for 12 h, and
subsequently cultured in culture medium with BBR (100 µM) for
a further 24 h, after which the total RNA was collected from the
cells. Finally, cells in the LPS+DMSO group were cultured in
culture medium with LPS (10 µg/ml) for 12 h, and subsequently
cultured in culture medium with DMSO (0.2%) for a further
24 h, after which the total RNA was collected from the cells.
BBR was dissolved in DMSO (0.2%). For all of the experiments,
IEC-18 cells (1x106) were cultured in DMEM containing 10%
fetal bovine serum (FBS) (Invitrogen; Thermo Fisher Scientic,
Inc.), 1% penicillin/streptomycin and 0.1 µg/ml insulin at 37˚C
under an atmosphere of 5% CO2. Total RNA was extracted from
the cells using TRIzol reagent, according to the manufacturer's
instructions, and genomic DNA was removed using DNase I
(Takara Biotechnology Co., Ltd.). Finally, the RNA quality
was determined using an Agilent 2100 Bioanalyzer (Agilent
Technologies, Inc.) and quantified using a NanoDrop 2000
instrument (NanoDrop Technologies; Thermo Fisher Scientic,
Inc.), according to the manufacturer's instructions.
MTT cell viability assay. The MTT assay was performed
following a previous experiment by Mosmann (21). Cells were
cultured in 96-well plates at a density of 1x104 cells per well. The
four groups were treated for 0, 4, 8, 12, 16, 20 and 24 h. After
incubation, 10 µl MTT (5 mg/ml in H2O) was added to each well
and incubation continued for a further 4 h at 37˚C. The culture
media containing MTT were aspirated and 150 µl DMSO was
then added into each well to dissolve the formazan crystals,
and subsequently the absorbance of each well was recorded
at 570 nm using a BioTek ELISA microplate reader (BioTek
Instruments, Inc.). Cell viability was estimated by dividing the
absorbance of treated cells in each well to the mean absorbance
of the control. The values were calculated from three indepen-
dent experiments. Data are presented as the mean ± SD (n=3).
Library preparation and Illumina HiSeq 4000 sequencing.
The RNA-seq transcriptome library was prepared following
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 5165
the TruSeq™ RNA sample preparation kit from Illumina,
Inc., using 5 µg total RNA. Libraries were selected for
cDNA target fragments of 200-300 bp on 2% low range
ultra agarose, followed by PCR amplication using Phusion
DNA polymerase (New England BioLabs, Inc.) for 15 PCR
c y c l e s ( f o r w a r d p r i m e r , 5 ' ‑ A G A T C G G A A G A G C A C A C G
T C ‑ 3 ' ; r e v e r s e p r i m e r , 5 ' A G A T C G G A A G A G C G T C G T G T ‑ 3 ' )
under the following thermocycling conditions: 50˚C for 2 min,
95˚C for 10 min, 95˚C for 30 sec and 60˚C for 30 sec (24).
After quantication using a TBS‑380 Mini‑Fluorometer, the
paired-end RNA-seq sequencing library was sequenced with
the Illumina HiSeq 4000 System (2x150 bp read length).
Data analysis. The expression of each transcript was
calculated according to the fragments per kilobase of
exon per million mapped reads (FPKM) method (24).
RNA-seq by expectation-maximization (version 2.2.0;
deweylab.biostat.wisc.edu/rsem/) was used to quantify the
abundance of the genes. DEGs were identified through
pairwise comparisons using EdgeR (Empirical analysis of
Digital Gene Expression in R) (version 4.0; bioconductor.
org/packages/release/bioc/html/edgeR.html). Genes having an
abundance with a fold‑change ≥2 and P<0.05 were considered
to be regulated differently in the four comparison groups
(control vs. LPS; LPS vs. LPS+BBR; LPS vs. LPS+DMSO; and
LPS+BBR vs. LPS+DMSO). To further investigate the biolog-
ical processes associated with DEGs, GO analysis (25,26)
was performed by running queries for each DEG against the
GO database, which provided information on the relevant
‘molecular functions’, ‘cellular components’ and ‘biological
processes’. KEGG functional-enrichment analysis (27) was
subsequently performed to identify the DEGs that were
signicantly enriched in anti‑inammatory pathways (P≤0.05)
compared with the whole-transcriptome background. Principal
component analysis (PCA) and hierarchical clustering analysis
(HCA) were also performed to assess the similarities and
differences in transcriptome proles using the online software
MetaboAnalyst 4.0 (metaboanalyst.ca/).
WGCNA analysis. WGCNA was performed on normalized
counts of RNA-Seq data. An adjacency matrix was built
with a soft thresholding value of 7, based on the recom-
mendation of the WGCNA tutorial (horvath.genetics.ucla.
edu/html/CoexpressionNetwork/Rpackages/WGCNA/index.
html). A gene cluster dendrogram was constructed with a
height cutoff of 0.25.
Interactive pathways explorer analysis. By using iPath3.0
(pathways.embl.de) to make a visual analysis of metabolic
pathways, the metabolic pathway information of the whole
biological system was viewed. The nodes represent different
compounds; boundaries represent different enzymatic
reactions.
Reverse transcription‑ quantitative PCR (RT‑qPCR). RT- qP C R
analysis was performed to validate the expression of crucial
DEGs. According to the manufacturer's instructions, total
RNA was extracted with TRIzol reagent (Invitrogen; Thermo
Fisher Scientic, Inc.) from different groups, including control
group, LPS‑stimulated inammatory models, and the BBR
and DMSO groups. Total RNA (2 µg) was reverse-transcribed
(25˚C for 10 min, 50˚C for 45 min and 85˚C for 5 min) into
single-stranded cDNA using HiScript Reverse Transcriptase
(Vazyme Biotech Co., Ltd.). RT-qPCR was subsequently
performed with the following thermocycling conditions:
50˚C for 2 min, 95˚C for 10 min, 95˚C for 30 sec and 60˚C
for 30 sec. All reactions were processed in triplicate for
40 cycles using a QuantStudio 6 Flex Real-Time PCR System
(Applied Biosystems; Thermo Fisher Scientic, Inc.) and the
uorophore was SYBR Green I (Thermo Fisher Scientic,
Inc.). The relative expression was calculated according to the
2-ΔΔCq method (28). The relevant oligonucleotide sequences of
primers are presented in Table SI. β-actin was used as a refer-
ence gene in RT-qPCR.
Statistical analysis. Results are presented as the mean ± SEM.
RNA-seq experimental data were analyzed by one-way
ANOVA followed by the Duncan's multiple comparison post
hoc test using GraphPad 8.0 software (GraphPad Software,
Inc.). P<0.05 was considered to indicate a statistically signi-
cant difference.
Results
Cell viability assay. In the present trial, an LPS-induced
inammatory cell model was established and then treated with
BBR. The anti‑inammatory effect of BBR was performed by
measuring the cell viability of IEC-18 cells using an MTT
assay. The cell viability of four groups is presented in Fig. 1.
The results showed that BBR can increase the cell viability of
IEC-18 cells, compared with the LPS and LPS+DMSO groups.
Gene identification. In the present study, an average of
52,040,757 raw reads from the control, LPS, LPS+BBR and
LPS+DMSO group samples were obtained, and the average
number of clean reads was 51,420,009. All the downstream
analyses were based on high-quality clean data and the error
rates were all <0.025%. The clean reads were mapped to the
mouse reference genome sequence, and 95.56-95.91% of the
clean reads in the libraries were mapped to the rat reference
genome (Table I).
Comparative transcriptomic analysis. To investigate the genes
of interest associated with anti‑inammation and their expres-
sion patterns, the coding genes among the different groups that
were expressed specically in the cells were compared and
characterized. In total, 11,732, 11,923, 11,829 and 11,953 genes
were found to have expression levels >0.1 FPKM in the control,
LPS, LPS+BBR and LPS+DMSO groups, respectively. The
expression of ~11,258 of the genes (90.4% of the total number
of coding genes) was shared by the four groups. On the other
hand, there were 117, 89, 114 and 118 genes expressed uniquely
in the control, LPS, LPS+BBR and LPS+DMSO groups,
respectively (Fig. 2A).
PCA was used to display the associations among the tran-
scriptomes representing the largest variance in the datasets.
As expected, replicates for each group were closer to each
other than they were to the other groups. Principal component
(PC)1, which accounted for 35.87% of the variance, separated
the control group from the other groups. PC2, which accounted
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5166
for 13.6% of the total variance, separated the BBR group from
all other groups (Fig. 2B). Of note, the transcriptomes of the
BBR group were found to be markedly different from those of
the LPS group, although they were close to those of the control
group; by contrast, the DMSO group was similar to the LPS
group.
HCA was performed to oversee the transcriptomic changes
within different samples from the control, LPS, LPS+BBR and
LPS+DMSO groups (Fig. 3A). The heatmap presents the rela-
tive abundance of the gene expression levels, where deep red
represents a higher intensity and deep blue represents a lower
intensity. Samples are displayed as columns, and different
colors were used to indicate the classication of the different
subtypes. Cell samples from the control and LPS groups, as
well as the LPS and LPS+BBR groups, displayed different
color distributions. Different repetitions from the same group
exhibited similar transcriptome distributions, and these were
rst aggregated into a cluster. With an increase in Euclidean
distance, the LPS+DMSO and LPS samples were aggregated
into a cluster, and these differed from the BBR and control
samples, suggesting that signicant changes had occurred in
the transcriptome following treatment with BBR.
In addition, the scatter diagram shown in Fig. 3B identies
the DEGs with different colors, in which red signies genes
that were upregulated, and green indicates those that were
downregulated. In pairwise comparisons between the control
and LPS samples, a total of 1,901 genes were found to be differ-
entially expressed, of which 1,289 genes were upregulated
and 612 were downregulated in the LPS group. In pairwise
comparisons between the LPS and LPS+BBR samples, a
total of 1,875 genes were differentially expressed, of which
687 genes were upregulated and 1,188 were downregulated in
the LPS+BBR group (Fig. 3B). It is noteworthy that the DEGs
between the control and LPS groups were very similar to
those between the LPS and LPS+BBR groups, although they
exhibited the opposite regulatory effects. Therefore, one may
speculate that BBR could be responsible for the occurrence of
biochemical events in different samples following treatment.
GO and KEGG pathway analyses. GO analysis not only
provides reliable gene product descriptions from various
databases, but it also offers a set of dynamic, controlled and
structured terminologies to describe gene functions and prod-
ucts in an organism. According to GO functions, all DEGs are
routinely classied into three categories: ‘Biological process’,
‘cellular component’ and ‘molecular function’. In the present
study, a total of 59 terms were found to be enriched in GO
terms (LPS group vs. the LPS+BBR group), among which 27
were for ‘biological process’, 17 were for ‘cellular component’,
and 15 were for ‘molecular function’ (Table SII). Concerning
the ‘biological process’ category, 77.68% genes were annotated
into ‘cellular process’ (GO:0009987), 56.82% genes were
involved in ‘biological regulation’ (GO:0065007), and 54.16%
genes were involved in ‘metabolic process’ (GO:0008152)
(Fig. 4). In terms of the ‘cellular component’ category, 75.27%
of the genes were located in ‘cell part’ (GO:0044464), and
42.82% were in ‘organelle part’ (GO:0044422) (Fig. 4). Finally,
regarding the ‘molecular function’ category, 69.12% genes
were involved in ‘binding’ (GO:0005488), whereas 34.14%
genes were in ‘catalytic activity’ (GO:0003824) (Fig. 4).
The DEGs (LPS vs. LPS+BBR) in the KEGG pathway
database were also mapped, and all the pathways were clas-
sied into the following six categories: ‘Metabolism’ (15.1%),
‘Genetic information processing’ (5.2%), ‘Environmental
information processing’ (15.5%), ‘Cellular processes’ (12.4%),
‘Organismal systems’ (19.4%) and ‘Human diseases’ (32.4%)
(Fig. 5).
To cha racterize the functional consequences of gene expres-
sion changes caused by BBR, GO enrichment analysis of 829
DEGs (LPS group vs. the LPS+BBR group) was performed
based on the GO database. Fig. 6A shows the top 20 ranked
GO terms of the DEGs. ‘DNA replication initiation’ showed
the highest enrichment degree as it possessed the highest rich
factor (0.54), followed by ‘kinetochore organization’ (rich
factor, 0.44). In addition, ‘nuclear chromosome segregation’
(0.23), ‘mitotic cell cycle’ (0.15) and ‘regulation of chromo-
some separation’ (0.27) were the most abundant functional
groups in the majority of the comparisons.
Subsequently, KEGG enrichment analysis was performed.
The results showed that most of the annotated genes involved
in the top 20 ranked KEGG pathways of DEGs were enriched
in ‘Steroid biosynthesis’ (rich factor, 0.36), ‘DNA replication’
(0.28), ‘TNF signali ng pathway’ (0.08), and ‘Cytokine- cytokine
receptor interaction’ (0.07) (Fig. 6B).
WGCNA analysis. The total number of 32,883 genes were
divided into 25 modules according to similarities in the expres-
sion patterns (Fig. 7A). The aim was to focus on the differences
between the LPS and the LPS+BBR treatment groups. The
results demonstrated that module ‘brown’ accorded closely
with the requirements of the present study, as the correlation
coefcient between module ‘brown’ and the LPS+BBR group
was 0.753 (Fig. 7B). To identify the key genes from module
‘brown’, a gene cor relation network was constructed using 1,794
genes in this module. Based on the degree of connectivity, the
top 20 genes were regarded as hub genes. The top 5 genes were
Vasorin (Vasn), activin receptor type-1B (Acvr1b), NF-kB
inhibitor a (Nfkbia), purine nucleoside phosphorylase (Pnp)
and disintegrin and metalloprotinease domain-containing
protein 17 (Adam17) (Fig. 7C). Vasm was associated with ‘cell
surface receptor signaling pathway’ (GO:0007166), and Acvr1b
with ‘regulation of transcription from RNA polymerase II
Figure 1. Cell viability assay. Cell viability was measured by an MTT assay,
IEC-18 cells were divided into four groups, and these groups were treated
for 0, 4, 8, 12, 16, 20 and 24 h. The cell viability was estimated by dividing
the absorbance of treated cells in each group to the mean absorbance of the
control. The values were calculated from three independent experiments.
Data are presented as the mean ± SD (n=3). LPS, lipopolysaccharide; BBR,
berberine.
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 5167
promoter’ (GO:0045944) and ‘positive regulation of activin
receptor signaling pathway’ (GO:0032927). Nfkbia was asso-
ciated with ‘regulation of NF-κB transcription factor activity’
(GO:0032088) and ‘Toll-like receptor 4 signaling pathway’
(GO:0034142). Pnp was associated with ‘regulation of a-b T cell
differentiation’ (GO:0046638) and ‘interleukin-2 secretion’
(GO:0070970). Adam17 was associated with ‘regulation of
protein phosphorylation’ (GO:0001934) and ‘Notch signaling
pathway’ (GO:0007219) (Table SIII). Subsequently, KEGG
enrichment analysis was performed on the genes involved
in module ‘brown’. The results revealed that the majority of
the annotated genes involved in the top 15 ranked KEGG
pathways of module ‘brown’ were enriched in ‘Endocytosis’,
‘TNF-signaling pathway’, ‘Chemokine signaling pathway’,
‘Toll-like receptor signaling pathway’ and ‘MAPK signaling
pathway’ (Fig. 8).
Metabolic network analysis. Interactive Pathways Explorer
(iPath) was employed to improve the understanding of the
global differential biological metabolic response between
the LPS and LPS+BBR groups. iPath analysis revealed the
presence of 538 DEGs, mainly focused on ‘Amino acid
metabolism’ (Fig. 9A), ‘Nucleotide metabolism’ (Fig. 9B)
and ‘Lipid metabolism’ (Fig. 9C).
Genes involved in DNA replication and cell cycle. A total of
27 genes associated with the cell cycle were detected with
signicantly different expression patterns between the LPS
and BBR samples (26 genes were downregulated and 1 was
upregulated in the BBR group) (Table II). The results of the
present study showed that the expression levels of cell division
cycle (Cdc) 6, origin recognition complex subunit (Orc) 1,
Orc6, minichromosome maintenance complex component
Figure 2. Correlation analysis of gene expression. (A) Venn analysis showing the number of co‑expression and specic expression genes between samples or
between groups. (B) PCA analysis was based on expression level clustering of samples. LPS, lipopolysaccharide; BBR, berberine; PCA, principal component
analysis.
Table I. Reads mapping summary of the four groups.
Sample Raw reads Clean reads Total mapped (%) Error rate, % Q20, % Q30, % GC content, %
Control 1 46,524,694 46,012,484 43,968,009 (95.56) 0.0239 98.42 95.31 51.44
Control 2 55,153,734 54,522,550 52,161,079 (95.67) 0.0241 98.34 95.11 51.22
Control 3 51,084,072 50,472,388 48,406,455 (95.91) 0.0246 98.16 94.64 51.60
LPS 1 54,040,422 53,383,494 51,070,101 (95.67) 0.0244 98.20 94.78 51.92
LPS 2 53,793,660 53,156,402 50,867,107 (95.69) 0.0242 98.31 95.04 51.87
LPS 3 48,673,560 48,055,566 45,998,851 (95.72) 0.0243 98.23 94.88 51.89
LPS+BBR 1 55,773,654 55,091,394 52,759,191 (95.77) 0.0244 98.22 94.83 52.65
LPS+BBR 2 51,508,862 50,889,204 48,767,843 (95.83) 0.0242 98.31 95.03 52.53
LPS+BBR 3 48,967,886 48,415,432 46,393,264 (95.82) 0.0240 98.36 95.16 52.16
LPS+DMSO 1 47,007,982 46,428,692 44,443,027 (95.72) 0.0241 98.32 95.08 51.97
LPS+DMSO 2 56,177,300 55,495,184 53,114,736 (95.71) 0.0243 98.26 94.92 52.43
LPS+DMSO 3 55,783,230 55,117,324 52,849,178 (95.88) 0.0244 98.22 94.81 52.12
LPS, lipopolysaccharide; BBR, berberine; G, guanine; C, cytosine.
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(Mcm) 3, Mcm4, Mcm5, Mcm6, Mcm7 and Cdc7were down-
regulated in the BBR group compared with the LPS group,
which suggested that BBR can restrict DNA replication, thereby
inhibiting the cell cycle by regulating these key genes.
Figure 4. GO annotations analysis. The differentially expressed genes between LPS and LPS+BBR groups were classied into ‘biological process’, ‘cellular
component’ and ‘molecular function’ (orange circle, ‘cellular process’; red circle, ‘biological regulation’, blue circle, ‘metabolic process’; yellow circle, ‘cell
part’; purple circle, ‘organelle part’; green circle, ‘binding’; black circle, ‘catalytic activity’). LPS, lipopolysaccharide; BBR, berberine; GO, Gene Ontology.
Figure 3. Four groups transcriptome data analysis. (A) Correlation analysis was used to test whether the variation between samples, especially between
biological replicates, was consistent with the experimental design. (B) Expression level difference scatter plot reects the difference in gene expression among
groups (red represents upregulation and green represents downregulation). LPS, lipopolysaccharide; BBR, berberine.
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 5169
Genes involved in apoptosis. A total of 19 genes associated
with apoptosis were detected with significantly different
expression levels, comparing between the LPS and BBR
groups (13 were downregulated, whereas 6 were upregulated,
Figure 5. KEGG annotations analysis. The DEGs between LPS and LPS+BBR groups were classied into ‘Metabolism’, ‘Genetic Information Processing’,
‘Environmental Information Processing’, ‘Cellular Processes’, ‘Organismal Systems’ and ‘Human Diseases’. Upregulated gene enrichment is shown on the left
and downregu lated gene enrichment on the r ight (red recta ngle, ‘amino acid metabolism’; orange rectangle, ‘signaling molecules and intera ction’; blue rectangle,
‘infectious diseases’). DEGs, differentially expressed genes; LPS, lipopolysaccharide; BBR, berberine; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Table II. Genes involved in DNA replication and cell cycle.
LPS_vs.
_LPS_BBR Gene ID Gene name Gene description
Down ENSRNOG00000000632 Cdk1 Cyclin-dependent kinase 1
Down ENSRNOG00000016708 Necab3 N-terminal EF-hand calcium binding protein 3
Down ENSRNOG00000024043 Orc6 Origin recognition complex subunit 6
Down ENSRNOG00000054057 AABR07058955.2 -
Down ENSRNOG00000000521 Cdkn1a Cyclin-dependent kinase inhibitor 1A
Down ENSRNOG00000005376 Mad2l1 Mitotic arrest decient 2 like 1
Down ENSRNOG00000050071 Cdc45 Cell division cycle 45
Down ENSRNOG00000014336 Mcm5 Minichromosome maintenance complex component 5
Down ENSRNOG00000003802 Pttg1 Pituitary tumor-transforming 1
Down ENSRNOG00000008841 Orc1 Origin recognition complex subunit 1
Down ENSRNOG00000012543 Mcm3 Minichromosome maintenance complex component 3
Down ENSRNOG00000007906 Bub1b BUB1 mitotic checkpoint serine/threonine kinase B
Down ENSRNOG00000002105 Cdc7 Cell division cycle 7
Down ENSRNOG00000008055 Ccne2 Cyclin E2
Down ENSRNOG00000028415 Cdc20 Cell division cycle 20
Down ENSRNOG00000015423 Ccna2 Cyclin A2
Down ENSRNOG00000029055 Ttk Ttk protein kinase
Down ENSRNOG00000003703 Mcm6 Minichromosome maintenance complex component 6
Down ENSRNOG00000018815 Plk1 Polo-like kinase 1
Down ENSRNOG00000053626 AABR07058955.1 -
Down ENSRNOG00000027787 Cdc6 Cell division cycle 6
Down ENSRNOG00000001349 Mcm7 Minichromosome maintenance complex component 7
Down ENSRNOG00000001833 Mcm4 Minichromosome maintenance complex component 4
Down ENSRNOG00000012835 Espl1 Extra spindle pole bodies like 1 separase
Down ENSRNOG00000002418 Tgfb2 Transforming growth factor β2
Down ENSRNOG00000008956 Cdkn2c Cyclin-dependent kinase inhibitor 2C
UP ENSRNOG00000061358 AC129365.1 -
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in the BBR group) (Table III). The results of the present study
indicated that the expression levels of cathepsin W (Ctsw),
inhibitors of apoptosis proteins (IAPs)-Birc5 and Bcl-2 were
downregulated, whereas cytochrome c testis (Cyct) and Bax
were upregulated, in the BBR group compared with the LPS
group, which suggested that, in the BBR group, more Cyct is
released from mitochondria into the cytosol of numerous cell
types undergoing apoptosis. Furthermore, a higher level of
caspase activation would result from the binding of Cyct to
apoptotic protease-activating factor 1 and pro-caspase 9, thus
promoting the formation of apoptosomes (29).
Genes involved in the TLR4/NF‑κB and MAPK/AP‑1 pathway.
A total of 56 genes associated with inammation were detected
that had signicantly different expression levels comparing
between the BBR and LPS groups (47 were downregulated,
Figure 6. GO and KEGG enrichment analysis. (A) Top 20 ranked GO terms of DEGs between LPS and LPS+BBR groups (red rectangle, ‘DNA replica-
tion initiation’; yellow rectangle, ‘Nuclear chromosome segregation’; blue rectangle, ‘Kinetochore organization’; orange rectangle, ‘Mitotic cell cycle’; pink
rectangle, ‘Regulation of chromosome separation’). (B) Top 20 ranked KEGG pathways of DEGs between LPS and LPS+BBR groups (red rectangle, ‘DNA
replication’; yellow rectangle, ‘Steroid biosynthesis’; orange rectangle, ‘Cytokine-cytokine receptor interaction’; blue rectangle, ‘TNF signaling pathway’).
DEGs, differentially expressed genes; LPS, lipopolysaccharide; BBR, berberine; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology.
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 5171
whereas 9 were upregulated, in the BBR group) (Table IV).
The results of the present study demonstrated that the expres-
sion levels of TLR4, myeloid differentiation primary response
protein MyD88 (MyD88), TNF receptor-associated factor 6
(TRAF6), interleukin-1 receptor-associated kinase (IRAK)4,
IRAK1, transforming growth factor-β-activated kinase
(TAK)1, mitogen-activated protein kinase kinase (MKK)3,
proto-oncogene c-Fos (c-Fos), c-Jun, MKK7, MAPK1 and
MAPK3 were downregulated in the BBR group compared with
the LPS group (Fig. 10B), suggesting that ‘classical’ inam-
matory pathways, such as the TLR4/NF-κB and MAPK/AP-1
pathways, were inhibited by BBR (Fig. 10A).
Genes involved in leukocyte migration. A total of 16 genes asso-
ciated with leukocyte migration were detected with signicantly
different expression levels, comparing between the BBR and LPS
groups (all downregulated in the BBR group) (Table V). The
results of the present study revealed that C-X-C motif chemokine
(Cxcl)1, Cxcl2, Cxcl3, Cxcl11, Cxcl9, C-C motif chemokine (Ccl)2,
Ccl12, integrin a-M, vascular cell adhesion molecule 1 (Vcam1),
Claudin-1, Cx3cl1 and intercellular cell adhesion molecule 1
(Icam1) were downregulated in the BBR group compared with
the LPS group, suggesting that BBR is able to inhibit leukocyte
migration via inhibiting chemokines and cell adhesion molecules,
thereby reducing the inltration of inammatory cells and the
harmful immune inammatory response.
Discussion
The biomolecular events of DNA replication are central to
diverse cellular processes, including development, cancer
etiology, drug treatment and resistance (30). Numerous
proteins and pat hways exist to ensure the delit y of DNA repli-
cation and protection of stalled or damaged replication forks.
Consistently, mutations in proteins involved in DNA replica-
tion are implicated in diverse diseases that include defects
Figure 7. Weighted Gene Correlation Network Analysis. (A) A total of 32,883 genes were divided into 25 modules according to the similarity in expression
patterns (the arrow points to the brown module). (B) The correlation between modules and groups. The abscissa represents different groups, and the ordinate
represents different modules. A column of numbers on the left of the gure represents the number of genes of the module, and each set of data on the right
represents the correlation coefcient and P‑value of the module and group. Red indicates a greater correlation between module and group, whereas blue
indicates a smaller correlation between module and group (red rectangle, module ‘brown’, the correction index is 0.753). (C) The top 20 hub genes of ‘brown’
module were obtained through the visualization network analysis, and the top 5 are labeled in red (Vasn, Adam17, Nf kbia, Pnp, Acvr1b). Vasn, Vasorin; Acvr1b,
activin receptor type-1B; Nfkbia, NF-κB inhibitor α; Pnp, purine nucleoside phosphorylase; Adam17, disintegrin and metalloprotinease domain-containing
prot ein 17.
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5172
during embryonic development and immunity, accelerated
aging, increased inammation, blood disease and cancer (23).
Precise duplication of genomic DNA is essential to maintain
genome stability and prevent genetic abnormalities associated
with cancer and other diseases. Accordingly, DNA replication
includes an ordered and highly regulated series of steps, both
before and during S phase (31). In preparation for S phase, DNA
replication origins are generated in a process termed replica-
tion licensing, which occurs during late mitosis and G1. During
this process, the ORC is recruited to specic genomic sites,
where it binds and recruits the ATPase CDC6 and chromatin
licensing and DNA replication factor 1, forming the pre-RC,
which, in turn, facilitates the loading of the heterohexameric
MCM2-7 complex onto chromatin (32-34). Once S phase
begins, the MCM complex is activated to serve as the replica-
tive helicase in association with CDC45 and DNA replication
complex GINS protein PSF1, unwinding DNA at the replication
fork (35,36). The replication fork is then loaded with prolifer-
ating cell nuclear antigen, a sliding processivity clamp for DNA
synthesis in association with the replicative polymerases DNA
polymerase d catalytic subunit and DNA polymerase e catalytic
subunit A (37). Once replication is initiated at a given origin, the
MCM helicase is displaced ahead of the replication fork, and is
therefore never associated with newly replicated DNA (38).
Cell cycle activation (CCA) occurs in secondary injury
after traumatic brain injury (TBI) (39). In postmitotic
cells, such as neurons, CCA contributes to programmed
cell death. In glia, CCA induces astrocyte and microglial
proliferation/reactivation, leading to astroglial scar forma-
tion, the release of pro‑inammatory cytokines and reactive
oxygen species (ROS), and ultimately, neuronal degenera-
tion (33-40). Administration of cell cycle inhibitors following
TBI increases neuronal survival and reduces microglial and
astroglial activation (41).
Previous studies have demonstrated that BBR induces
signicant mitochondrial apoptosis, G0/G1 cell cycle arrest
and inhibitive migration in thyroid carcinoma cells via the
phosphoinositide 3-kinase/AKT and MAPK signaling path-
ways (42). According to the transcriptome data in the present
study, it is possible to hypothesize that BBR is able to inuence
the expression of key proteins, such as CDC6, ORC, MCM,
CDC7, CycA, CycE and E2F, in the DNA replication process
to cause cell cycle arrest.
Previous studies have revealed that ROS damage is the
primary cause of cell death: The overexpression of Bcl-2 can
reduce the production of oxygen radicals and the formation of
lipid peroxides (43). These results suggest that the antioxidant
effect of Bcl-2 is indirect; that is, it may lie in inhibiting the
production of superoxide anions rather than in directly elimi-
nating ROS (44). Cyt c is an important electron transporter
in the respiratory chain. The release of Cyt c from the inner
membrane of mitochondria blocks the function of the respira-
tory chain, leading to an accelerated production of superoxide
anions (45). However, Bcl-2 is able to inhibit the release of
Cyt c, thus inhibiting the production of superoxide anion (46).
In addition, Bcl-2 can also increase the level of intracel-
lular glutathione (GSH) and other antioxidants, increase the
NAD/NADH ratio, inhibit the decrease of apoptosis-associated
GSH and promote the entry of GSH into the nucleus, thereby
affecting the redox state of cells (47). Programmed cell death,
Figure 8. Gene enrichment chord analysis. Genes involved in module ‘brown’ were analyzed via KEGG enrichment analysis (red rectangle, ‘Endocytosis’;
blue rectangle, ‘TNF signaling pathway’; yellow rectangle, ‘Chemokine signaling pathway’; green rectangle, ‘Toll-like receptor signaling pathway’; purple
rectangle, ‘MAPK signaling pathway’). KEGG, Kyoto Encyclopedia of Genes and Genomes.
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 517 3
or apoptosis, is a major regulator of cell number and tissue
homeostasis (48). Apoptosis is tightly controlled through the
action of both activators and inhibitors of caspases (49). The
best studied family of caspase inhibitors are the IAPs. Nitric
Figure 9. Interactive Pathways Explorer analysis. By visualizing the metabolic pathways involved in the DEGs between LPS and LPS + berberine groups, the
red metabolic pathways were enriched by DEGS. (A) ‘Amino acid metabolism’ (black circle). (B) ‘Nucleotide metabolism’ (black circle). (C) ‘Lipid metabo-
lism’ (black circle). DEGs, differentially expressed genes; LPS, lipopolysaccharide.
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5174
oxide (NO)-induced apoptosis is associated with the down-
regulation of IAP expression, which facilitates the activation
of the caspase cascade and subsequent poly-ADP-ribose poly-
merase (PARP) cleavage (50).
A previous study reported that, in cells treated with BBR,
the expression levels of Bax and PARP cleavage were increased,
whereas the expression level of Bcl-2 was reduced (51). BBR
has also been demonstrated to induce dose-dependent quies-
cence and apoptosis in A549 cancer cells through modulating
cell cyclins and inammation independently of the mTOR
pathway (52). The results of the present study were consistent
with these previous reports. According to the transcriptome
data of the present study, it was possible to speculate that BBR
may regulate Bax/Bcl-2 gene expression by downregulating
cathepsin and IAPs, causing mitochondria to release exces-
sive levels of Cyt c that induce apoptosis, thereby combatting
inammatory damage.
In previous studies, BBR has been found to be able to
inhibit LPS‑induced expression of inammatory cytokines by
suppressing the TLR4-mediated NF-κB and MAPK signaling
pathways in rumen epithelial cells (53). BBR was also reported
to inhibit AP-1 activity in a dose- and time-dependent manner.
BBR concentrations as low as 10 µM were found to inhibit AP-1
activity almost completely following 48 h treatment (54). These
results, together with those of the present study, demonstrate
that BBR exerts a signicant inuence on the TLR4/NF‑κB
and MAPK/AP-1 pathway. The transcriptome data of the
present study revealed the role of BBR in both pathways
more comprehensively, further clarifying the functional genes
that are located upstream or downstream in these pathways.
According to a previous study, the TLR4-mediated response to
LPS can be divided into two types: An early MyD88-dependent
response and a delayed MyD88-independent response (55).
Downstream events in the activation of the MyD88-dependent
pathway are elicited by LPS, leading to activation of the NF-κB
and MAPK pathways. A typical model of the activation of
NF-κB is initiated by the binding of IRAK-1 and IRAK-4 to
the receptor complex. The phosphorylation of IRAK-1 occurs
in two substeps, giving rise to hyperphosphorylated IRAK-1,
which separates IRAK-1 from the receptor complex, causing
it to bind with TRAF6 (56). TRAF6 subsequently becomes
activated and associates with TGF-β-activated kinase 1
MAP3K7-binding protein (TAB)2 to activate the MAPK
kinase, TAK1, which is constitutively associated with its
adaptor protein, TAB-1 (57,58). At this point, TAK-1 acts as a
common activator of NF-κB, as well as of the c-Jun N-terminal
kinase (JNK) and p38 pathways (59). The activation of NF-κB
is initiated by the assembly of a high-molecular-weight protein
complex known as the signalosome. This complex comprises
IKKα and IKKβ, together with a scaffolding protein named
IKKγ (also known as NEMO). Subsequent phosphorylation
of a set of IκBs results in their degradation and ubiquitina-
tion, releasing NF-κB factor, which can then translocate to
the nucleus. MAPKs are highly conserved protein threo-
nine/serine kinases, and three major subfamilies, including
extracellular signal-regulating kinases (ERKs) 1 and 2, JNK
and p38, have been found in mammalian cells (59-61). MAPKs
have been demonstrated to be involved in pro‑inammatory
signaling pathways, and abundant evidence has demonstrated
that the activation of ERK1/2, JNK and p38 is involved in the
upregulation of TNF-α, inducible nitric oxide synthase, IL-6
and COX-2 in LPS-activated macrophages. ERK1/2 and JNK
Table III. Genes involved in apoptosis.
LPS_vs.
_LPS_BBR (regulate) Gene ID Gene name Gene description
Down ENSRNOG00000013774 Lmnb1 Lamin B1
Down ENSRNOG00000007529 Bmf Bcl2 modifying factor
Down ENSRNOG00000016571 Ngf Nerve growth factor
Down ENSRNOG00000027096 Ctsw Cathepsin W
Down ENSRNOG00000050819 Birc5 Baculoviral IAP repeat-containing 5
Down ENSRNOG00000003537 Spta1 Spectrin α erythrocytic 1
Down ENSRNOG00000002791 Bcl-2 BCL2 apoptosis regulator
Down ENSRNOG00000022521 Ddias DNA damage-induced apoptosis suppressor
Down ENSRNOG00000007367 Sept4 Septin 4
Down ENSRNOG00000058834 LOC103692471 Uncharacterized LOC103692471
Down ENSRNOG00000053339 AABR07062512.1 -
Down ENSRNOG00000012473 Car CASP8 and FADD‑like apoptosis regulator
Down ENSRNOG00000060728 Tuba1a Tubulin α1A
Up ENSRNOG00000023463 Parp9 Poly (ADP-ribose) polymerase family member 9
Up ENSRNOG00000003084 Parp1 Poly (ADP-ribose) polymerase 1
Up ENSRNOG00000008892 Parp2 Poly (ADP-ribose) polymerase 2
Up ENSRNOG00000024457 Cyt c Cytochrome c testis
Up ENSRNOG00000020876 Bax BCL2 associated X apoptosis regulator
Up ENSRNOG00000007529 Bmf Bcl2 modifying factor
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 517 5
Table IV. Genes involved in the TLR4/NF-kB and MAPK/AP-1 pathways.
LPS_vs.
_LPS_BBR Gene ID Gene name Gene description
Down ENSRNOG00000007390 Nfkbia NFKB inhibitor α
Down ENSRNOG00000008859 Tank TRAF family member-associated NFKB activator
Down ENSRNOG00000008565 Nkiras1 NFKB inhibitor interacting Ras-like 1
Down ENSRNOG00000053813 Nkap NFKB activating protein
Down ENSRNOG00000061989 Nkrf NFKB repressing factor
Down ENSRNOG00000005965 Irak4 interleukin-1 receptor-associated kinase 4
Down ENSRNOG00000020063 Nfkbib NFKB inhibitor β
Down ENSRNOG00000025111 Nfkbid NFKB inhibitor δ
Down ENSRNOG00000016010 Mul1 Mitochondrial E3 ubiquitin protein ligase 1
Down ENSRNOG00000019907 Nfkbie NFKB inhibitor ε
Down ENSRNOG00000056708 Nkapl NFKB activating protein-like
Down ENSRNOG00000004639 Traf6 TNF receptor associated factor 6
Down ENSRNOG00000023258 Nfkb1 Nuclear factor κB subunit 1
Down ENSRNOG00000018095 Nkiras2 NFKB inhibitor interacting Ras-like 2
Down ENSRNOG00000000839 Nfkbil1 NFKB inhibitor like 1
Down ENSRNOG00000014703 Tonsl Tonsoku-like DNA repair protein
Down ENSRNOG00000019311 Nfkb2 Nuclear factor κB subunit 2
Down ENSRNOG00000060869 Irak1 Interleukin-1 receptor-associated kinase 1
Down ENSRNOG00000010522 Tlr4 Toll-like receptor 4
Down ENSRNOG00000019073 Ikbkb Inhibitor of nuclear factor κB kinase subunit β
Down ENSRNOG00000007159 Ccl2 C-C motif chemokine ligand 2
Down ENSRNOG00000004553 Cox2 Cytochrome c oxidase assembly factor COX2
Down ENSRNOG00000014454 Ap1m1 Adaptor related protein complex 1 subunit µ1
Down ENSRNOG00000002061 Ptpn13 Protein tyrosine phosphatase non-receptor type
Down ENSRNOG00000038686 Ap1s2 Adaptor related protein complex 1 subunit σ2
Down ENSRNOG00000001415 Ap1s1 Adaptor related protein complex 1 subunit σ1
Down ENSRNOG00000061543 Ap2b1 Adaptor related protein complex 2 subunit β1
Down ENSRNOG00000013634 Myd88 MYD88 innate immune signal transduction adaptor
Down ENSRNOG00000012701 Map7 Microtubule-associated protein 7
Down ENSRNOG00000019568 Jund JunD proto-oncogene AP-1 transcription factor subunit
Down ENSRNOG00000029456 Rp9 RP9 pre-mRNA splicing factor
Down ENSRNOG00000013690 Clba1 Clathrin binding box of aftiphilin containing 1
Down ENSRNOG00000027831 Map7d3 MAP7 domain containing 3
Down ENSRNOG00000047516 Map3k7 Mitogen activated protein kinase kinase kinase 7
Down ENSRNOG00000005411 Aftph Aftiphilin
Down ENSRNOG00000032463 Rap1a RAP1A member of RAS oncogene family
Down ENSRNOG00000008786 Ap1b1 Adaptor related protein complex 1 subunit β1
Down ENSRNOG00000020552 Fosl1 FOS like 1 AP-1 transcription factor subunit
Down ENSRNOG00000001849 Mapk1 Mitogen activated protein kinase 1
Down ENSRNOG00000053583 Mapk3 Mitogen activated protein kinase 3
Down ENSRNOG00000010237 Map7d1 MAP7 domain containing 1
Down ENSRNOG00000046667 Fosb FosB proto-oncogene AP-1 transcription factor subunit
Down ENSRNOG00000006789 Ddit3 DNA-damage inducible transcript 3
Down ENSRNOG00000005176 Map7d2 MAP7 domain containing 2
Down ENSRNOG00000007048 Rap1b RAP1B member of RAS oncogene family
Down ENSRNOG00000026293 Jun Jun proto-oncogene AP-1 transcription factor subunit
Down ENSRNOG00000024492 Ap1ar Adaptor-related protein complex 1 associated regulatory protein
Up ENSRNOG00000014258 Rab32 RAB32 member RAS oncogene family
Up ENSRNOG00000049873 Ap1s3 Adaptor related protein complex 1 subunit σ3
Up ENSRNOG00000017871 Sidt2 SID1 transmembrane family member 2
Up ENSRNOG00000052357 Fosl2 FOS like 2 AP-1 transcription factor subunit
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5176
then promote the combination of c-Jun and c-Fos, which in
turn activates AP-1 (62).
Directional migration of leukocytes is crucial in innate
immunity for host defense. However, the recruitment of
leukocytes at the site of tissue injury are a leading cause of
the inammatory response (63). Chemokines have emerged as
the most important regulators of leukocyte trafcking during
in ammation. A number of chemokines have been implicated in
the pathogenesis of IBD (64). Upon detecting external stimuli,
IECs have the potential to secrete chemokines that can recruit
immune cells and directly induce the secretion of inammatory
cytokines, which augment and prolong inam matory responses.
For example, CXCL8, which is secreted from IECs and immune
cells and is considered to be a major chemotactic factor, can
attract C-X-C chemokine receptor type (CXCR)1(+)/CXCR2(+)
IL-23-producing neutrophils that infiltrate and accumulate
in inamed colon tissue (65). ICAM‑1 and VCAM‑1 are two
important members of the immunoglobulin gene superfamily,
although they have different roles in the adhesion of leukocytes
to the vascular endothelium. ICAM-1 can promote adhesion at
the site of inammation, thereby controlling cancer progres-
sion and regulating immune responses in the tissue. These
membrane proteins are necessary for anchoring leukocytes to
the vessel wall (66). Upregulated expression of claudin-1, which
is involved in early stages of transformation in IBD-associated
neoplasia (67). At present, few studies have been conducted on
the potential anti‑inammatory ability of BBR in downregu-
lating the expression of chemokines.
Previous studies have demonstrated that BBR was able
to reverse chronic inammatory pain induced by Complete
Freund's adjuvant, which alleviated comorbid depres-
sion (68,69). Its anti-nociceptive and anti-depressive effects
may be associated with the downregulated spinal levels of
the inflammatory cytokines and mRNA transcription of
CCL2 (70). The results of the present study showed that the
anti‑inammatory mechanism of BBR is likely to be associ-
ated with the regulation of chemokines and the migration of
leukocytes, which may provide a novel perspective for future
studies.
Recently, a large number of publications have focused
on the relationship between host metabolism and inamma-
tion (71,72). Atherosclerosis is a lipid- and immune cell-dr iven
chronic inammatory disease that is characterized by endo-
thelial dysfunction and defective non-revolving immune
responses. Arginine, L-homoarginine and L-tryptophan
metabolism have been revealed to exert an influence on
Table IV. Continued.
LPS_vs.
_LPS_BBR Gene ID Gene name Gene description
Up ENSRNOG00000000151 Ldlrap1 Low density lipoprotein receptor adaptor protein 1
Up ENSRNOG00000016769 Rab38 RAB38 member RAS oncogene family
Up ENSRNOG00000042838 Junb JunB proto-oncogene AP-1 transcription factor subunit
Up ENSRNOG00000025619 Ap1g2 Adaptor related protein complex 1 subunit γ2
Up ENSRNOG00000008015 Fos Fos proto-oncogene AP-1 transcription factor subunit
Table V. Genes involved in leukocyte migration.
LPS_vs._LPS_BBR Gene ID Gene name Gene description
Down ENSRNOG00000014333 Vcam1 Vascular cell adhesion molecule 1
Down ENSRNOG00000019728 Itgam Integrin subunit αM
Down ENSRNOG00000017539 Mmp9 Matrix metallopeptidase 9
Down ENSRNOG00000001926 Cldn1 Claudin 1
Down ENSRNOG00000006984 Mapk11 Mitogen-activated protein kinase 11
Down ENSRNOG00000016695 Mmp2 Matrix metallopeptidase 2
Down ENSRNOG00000020246 Myl9 Myosin light chain 9
Down ENSRNOG00000022298 Cxcl11 C-X-C motif chemokine ligand 11
Down ENSRNOG00000028043 Cxcl3 Chemokine (C-X-C motif) ligand 3
Down ENSRNOG00000002792 Cxcl2 C-X-C motif chemokine ligand 2
Down ENSRNOG00000002802 Cxcl1 C-X-C motif chemokine ligand 1
Down ENSRNOG00000022242 Cxcl9 C-X-C motif chemokine ligand 9
Down ENSRNOG00000007159 Ccl2 C-C motif chemokine ligand 2
Down ENSRNOG00000029768 Ccl12 Chemokine (C-C motif) ligand 12
Down ENSRNOG00000016326 Cx3cl1 C-X3-C motif chemokine ligand 1
Down ENSRNOG00000020679 Icam1 Intercellular adhesion molecule 1
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 517 7
immune regulation in endothelial, as well as innate and
adaptive immune cells, and their metabolites may be consid-
ered as putative therapeutic targets in chronic inammatory
disease (73). Whey protein hydrolysate and branched-chain
amino acids downregulate inammation‑associated genes in
vascular endothelial cells (74). The iPath metabolic network
analysis of the present study revealed the potential associa-
tion between BBR and amino acid metabolism, nucleotide
Figure 10. Anti‑inammatory effects of BBR on LPS‑induced inammation via suppression of the TLR4/NF‑κB and MAPK/AP-1 pathways. (A) Based on
the transcriptome data and Kyoto Encyclopedia of Genes and Genomes pathway database, a diagram of the TLR4/NF-κB and MAPK/AP-1 pathways was
constructed. (B) Reverse transcription-quantitative PCR was used to verify the key genes, including IRAK4, IRAK1, TAK1, TRAF6, MKK3, TLR4, MyD88,
c-Fos, c-Jun, MKK7, MAPK1 and MAPK3 in IEC-18 cells, the results were analyzed by an unpaired t-test with GraphPad 8.0 software. *P<0.05 and **P<0. 01.
LPS, lipopolysaccharide; BBR, berberine; TLR4, toll-like receptor 4; MyD88, myeloid differentiation primary response protein; IRAK4, interleukin-1
receptor-associated kinase; TAK1, transforming growth factor-β-activated kinase 1; TRAF6, TNF receptor-associated factor 6; MKK3, mitogen-activated
protein kinase kinase 3; c-Fos, proto-oncogene c-Fos.
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5178
metabolism and lipid metabolism, which may provide a new
explanation for BBR's anti‑inammatory effects.
BBR has already been approved for clinical therapy in China,
and a recent large-scale double-blind clinical trial has reported
that BBR is safe and effective to prevent colorectal cancer (75).
However, BBR has not been approved by FDA since the mecha-
nism underlying its anti‑inammatory activity remains poorly
understood. As an anti-inflammatory drug, its targets and
mechanisms are complex and diverse, so it is necessary to study
the drug from a wide range of different perspectives. In addition
to the classical regulation of gene expression, it is now possible
to explain the action of BBR at the level of metabolism or the
level of intestinal microorganisms. Despite some signicant
progress that has been made in this regard with the ndings
of the present study, there were also certain limitations; for
example, not having set multiple sampling time points and drug
concentrations. Also, the results of this study would be more
meaningful if samples from animals or human IECs had been
used. Therefore, it is necessary to perform an in-depth explora-
tion of this topic in the future.
Acknowledgements
Not applicable.
Funding
This project was supported by School Independent Innovation
Fund (Huazhong Agricultural University) grants (grant
no. 2662019PY059).
Availability of data and materials
The datasets generated during the current study are available
in the National Center for Biotechnology Information database:
ncbi.nlm.nih.gov/ [SRP254018].
Authors' contributions
XX, LZ, YZ and LM designed the experiment. XX, BX, WQ, YY,
CX and BY analyzed the data. XX and LM wrote and revised the
article. All authors read and approved the nal manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
1. Fest J, Ruiter R, Mulder M, Groot Koerkamp B, Ikram MA,
St r ick e r BH and van Eij ck CHJ: Th e systemic im mu ne‑inam m a -
tion index is associated with a n increased risk of incident ca ncer-A
population-based cohort study. Int J Cancer 146: 692-698, 2020.
2. Das S, Reddy MA, Senapati P, Stapleton K, Lanting L, Wang M,
Amaram V, Ganguly R, Zhang L, Devaraj S, et al: Diabetes
mellitus-induced long noncoding RNA Dnm3os regulates
macrophage functions and inflammation via nuclear mecha-
nisms. Arterioscler Thromb Vasc Biol 38: 1806-1820, 2018.
3. Sorriento D and Iaccarino G: Inammation and cardiovascular
diseases: The most recent ndings. Int J Mol Sci 20: 3879, 2019.
4. Dragasevic S, Stankovic B, Kotur N, Sokic-Milutinovic A,
Milovanovic T, Lukic S, Milosavljevic T, Srzentic Drazilov S,
Klaassen K, Pavlovic S and Popovic D: Metabolic syndrome in
inammatory bowel disease: Association with genetic markers
of obesity and inflammation. Metab Syndr Relat Disord 18:
31-38, 2020.
5. Bian Y, Dong Y, Sun J, Sun M, Hou Q, Lai Y and Zhang B:
Protective effect of kaempferol on LPS‑induced inammation
and barrier dysfunction in a coculture model of intestinal epithe-
lial cells and intestinal microvascular endothelial cells. J Agric
Food Chem 68: 160-167, 2020.
6. Huttenhower C, Kostic AD and Xavier RJ: Inammatory bowel
disease as a model for translating the microbiome. Immunity 40:
843-854, 2014.
7. Vong LB, Mo J, Abrahamsson B and Nagasaki Y: Specific
accumulation of orally administered redox nanotherapeutics in
the inamed colon reducing inammation with dose‑response
efcacy. J Control Release 210: 19‑25, 2015.
8. Aziz DA, Moin M, Majeed A, Sadiq K and Biloo AG: Paediatric
inammatory bowel disease: Clinical presentation and disease
location. Pak J Med Sci 33: 793-797, 2017.
9. Peterson LW and Artis D: Intestinal epithelial cells: Regulators of
barrier function and immune homeostasis. Nat Rev Immunol 14:
141-153, 2014.
10. Allaire JM, Crowley SM, Law HT, Chang SY, Ko HJ and
Vallance BA: The intestinal epithelium: Central coordinator of
mucosal immunity. Trends Immunol 39: 677-696, 2018.
11. Zeng XZ, Zhang YY, Yang Q, Wang S, Zou BH, Tan YH, Zou M,
Liu SW and Li XJ: Artesunate attenuates LPS-induced osteoclas-
togenesis by suppressing TLR4/TRAF6 and PLCγ1-Ca 2+-NFATc1
signaling pathway. Acta Pharmacol Sin 41: 229-236, 2020.
12. Fang F and Jiang D: IL-1β/HMGB1 signalling promotes the
inammatory cytokines release via TLR signalling in human
intervertebral disc cells. Biosci Rep 36: e00379, 2016.
13. Yeh M, Granger DN and Glass J: Increases in IKK-A activity,
IKB-A phosphorylation and degradation and p50 subunit
production precede nfkb activation in the intestine of rats after
LPS administration. Gastroenterology 118: PA819, 2000.
14. Shirwaikar A, Shirwaikar A, Rajendran K and Punitha IS:
In vitro antioxidant studies on the benzyl tetra isoquinoline
alkaloid berberine. Biol Pharm Bull 29: 1906-1910, 2006.
15. Tang J, Feng Y, Tsao S, Wang N, Curtain R and Wang Y:
Berberine and Coptidis rhizoma as novel antineoplastic agents:
A review of traditional use and biomedical investigations.
J Ethnopharmacol 126: 5-17, 2009.
16. Germoush MO and Mahmoud AM: Berberine mitigates
cyclophosphamide-induced hepatotoxicity by modulating anti-
oxidant status and inammatory cytokines. J Cancer Res Clin
Oncol 140: 1103-1109, 2014.
17. Liu YF, Wen CY, Chen Z, Wang Y, Huang Y and Tu SH: Effects
of berberine on NLRP3 and IL-1β expressions in monocytic
THP‑1 cells with monosodium urate crystals‑induced inam-
mation. Biomed Res Int 2016: 2503703, 2016.
18. Kuo CL, Chi CW and Liu TY: The anti‑inammatory potential
of berberine in vitro and in vivo. Cancer Lett 203: 127-137,
2004.
19. Bae YA and Cheon HG: Activating transcription factor-3 induc-
tion is involved in the anti‑inammatory action of berberine in
RAW264.7 mur ine macrophages. Korean J Physiol Pharmacol 20:
415-42 4, 2 016.
20. Zhang H, Shan Y, Wu Y, Xu C, Yu X, Zhao J, Yan J and Shang W:
Berberine suppresses LPS‑induced inammation through modu-
lating Sirt1/NF-κB signaling pathway in RAW264.7 cells. Int
Immunopharmacol 52: 93-100, 2017.
21. Li H, Fan C, Lu H, Feng C, He P, Yang X, Xiang C, Zuo J and
Tang W: Protective role of berberine on ulcerative colitis through
modulating enteric glial cells-intestinal epithelial cells-immune
cells interactions. Acta Pharm Sin B 10: 447-461, 2020.
22. Jing W, Safarpour Y, Zhang T, Guo P, Chen G, Wu X, Fu Q and
Wang Y: Berberine upregulates P-Glycoprotein in human caco-2
cells and in an experimental model of colitis in the rat via activa-
tion of Nrf2-dependent mechanisms. J Pharmacol Exp Ther 366:
332-340, 2018.
MOLECULAR MEDICINE REPORTS 22: 5163-5180, 2020 517 9
23. Li C, Xi Y, Li S, Zhao Q, Cheng W, Wang Z, Zhong J, Niu X
and Chen G: Berberine ameliorates TNBS induced colitis by
inhibiting inammatory responses and Th1/Th17 differentiation.
Mol Immunol 67: 444-454, 2015.
24. Pengyu Z, Yan Y, Xiying F, Maoguang Y, Mo L, Yan C, Hong S,
Lijuan W, Xiujuan Z and Hanqing C: The differential expression
of long noncoding RNAs in type 2 diabetes mellitus and latent
autoimmune diabetes in adults. Int J Endocrinol 2020: 9235329,
2020.
25. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H,
Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene
ontology: Tool for the unication of biology. The gene ontology
consortium. Nat Genet 25: 25-29, 2000.
26. The Gene Ontology Consortium: The gene ontology resource:
20 years and still GOing strong. Nucleic Acids Res 47: D330-D338,
2019.
27. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M:
KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids
Res 27: 29-34, 1999.
28. Chang S, Chen W and Yang J: Another formula for calculating the
gene change rate in real-time RT-PCR. Mol Biol Rep 36: 2165-2168,
20 09.
29. Zhang Y, Zhao L, Li X, Wang Y, Yao J, Wang H, Li F, Li Z and
Guo Q: V8, a newly synthetic avonoid, induces apoptosis through
ROS-mediated ER stress pathway in hepatocellular carcinoma. Arch
Toxicol 88: 97-107, 2014.
30. Mosmann T: Rapid colorimetric assay for cellular growth and
survival: Application to proliferation and cytotoxicity assays.
J Immunol Methods 65: 55-63, 1983.
31. Loeb LA and Monnat RJ Jr: DNA polymerases and human disease.
Nature Rev Genet 9: 594-604, 2008.
32. Zeman MK and Cimprich KA: Causes and consequences of replica-
tion stress. Nat Cell Biol 16: 2-9, 2014.
33. Blow JJ and Gillespie PJ: Replication licensing and cancer-a fatal
entanglement? Nat Rev Cancer 8: 799-806, 2008.
34. Mendez J and Stillman B: Perpetuating the double helix: Molecular
machines at eukaryotic DNA replication origins. Bioessays 25:
1158-1167, 2003.
35. Blow JJ and Dutta A: Preventing re-replication of chromosomal
DNA. Nature reviews. Nat Rev Mol Cell Biol 6: 476-486, 2005.
36. Arias EE and Walter JC: Strength in numbers: Preventing rereplica-
tion via multiple mechanisms in eukaryotic cells. Genes Dev 21:
49 7-518, 20 07.
37. Moyer SE, Lewis PW and Botchan MR: Isolation of the
Cdc45/Mcm2-7/GINS (CMG) complex, a candidate for the eukary-
otic DNA replication fork helicase. Proc Natl Acad Sci USA 103:
10236-10241, 2006.
38. Ilves I, Petojevic T, Pesavento JJ and Botchan MR: Activation of the
MCM2-7 helicase by association with Cdc45 and GINS proteins.
Mol Cell 37: 247-258, 2010.
39. Moldovan GL, Pfander B and Jentsch S: PCNA, the maestro of the
replication fork. Cell 129: 665-679, 2007.
40. Cer nak I, Stoica B, Byrnes KR, Di Giovanni S and Faden AI: Role of
the cell cycle in the pathobiology of central nervous system trauma.
Cell Cycle 4: 1286-1293, 2005.
41. Stoica BA, Byrnes KR and Faden AI: Cell cycle activation and CNS
inj u ry. Neurotox Res 16: 221-237, 2009.
42. Li L, Wang X, Sharvan R, Gao J and Qu S: Berberine could inhibit
thyroid carcinoma cells by inducing mitochondrial apoptosis, G0/G1
cell cycle arrest and suppressing m igration via PI3K-A KT and MAPK
signaling pathways. Biomed Pharmacother 95: 1225 -1231, 2 017.
43. Chong SJ, Low IC and Pervaiz S: Mitochondrial ROS and involve-
ment of Bcl-2 as a mitochondrial ROS regulator. Mitochondrion 19:
39-48, 2014.
44. Hashemi-Niasari F, Rabbani-Chadegani A, Razmi M and Fallah S:
Synergy of theophylline reduces necrotic effect of berber ine, induces
cell cycle arrest and PARP, HMGB1, Bcl-2 family med iated apoptosis
in MDA-MB-231 breast cancer cells. Biomed Pharmacother 106:
858-867, 2018.
45. PLOS ONE Editors: Retraction: Bak compensated for bax in
p53-null cells to release cytochrome c for the initiation of mito-
chondrial signaling during withanolide D-induced apoptosis. PLoS
One 15: e0228839, 2020.
46. Neame SJ, Rubin LL and Philpott KL: Blocking cytochrome c
activity within intact neurons inhibits apoptosis. J Cell Biol 142:
1583-1593, 1998.
47. Chauhan D, Pandey P, Ogata A, Teoh G, Krett N, Halgren R,
Rosen S, Kufe D, Kharbanda S and Anderson K: Cytochrome
c-dependent and -independent induction of apoptosis in multiple
myeloma cells. J Biol Chem 272: 29995-29997, 1997.
48. Campisi L, Cummings RJ and Blander JM: Death-defining
immune responses after apoptosis. Am J Transplant 14:
1488-1498, 2014.
49. Song Z and Steller H: Death by design: Mechanism and control
of apoptosis. Trends Cell Biol 9: M49-M52, 1999.
50. Stanford A, Chen Y, Zhang XR, Hoffman R, Zamora R and
Ford HR: Nitric oxide mediates dendritic cell apoptosis by
downregulating inhibitors of apoptosis proteins and upregulating
effector caspase activity. Surgery 130: 326-332, 2001.
51. Fu L, Chen W, Guo W, Wang J, Tian Y, Shi D, Zhang X, Qiu H, Xiao X,
Kang T, et al: Berberine targets AP-2/hTERT, NF-κB/COX-2,
HI F-1α/VEGF and Cytochrome-c/Caspase signaling to suppress
human cancer cell growth. PLoS One 8: e69240, 2013.
52. Kalaiarasi A, Anusha C, Sankar R, Rajasekaran S, John
Marshal J, Muthusamy K and Ravikumar V: Plant isoquinoline
alkaloid berberine exhibits chromatin remodeling by modulation
of histone deacetylase to induce growth arrest and apoptosis in
the A549 cell line. J Agric Food Chem 64: 9542-9550, 2016.
53. Zhao C, Wang Y, Yuan X, Sun G, Shen B, Xu F, Fan G, Jin M,
Li X and Liu G: Berberine inhibits lipopolysaccharide-induced
expression of inflammatory cytokines by suppressing
TLR4‑mediated NF‑ĸB and MAPK signaling pathways in rumen
epithelial cells of Holstein calves. J Dairy Res 86: 171-176, 2019.
54. Kim S, Choi JH, Kim JB, Nam SJ, Yang JH, Kim JH and Lee JE:
Berberine suppresses TNF-alpha-induced MMP-9 and cell inva-
sion through inhibition of AP-1 activity in MDA-MB-231 human
breast cancer cells. Molecules 13: 2975-2985, 2008.
55. Güney Eskiler G, Deveci Özkan A, Kaleli S and Bilir C: Inhibition
of TLR4/TRIF/IRF3 signaling pathway by curcumin in breast
cancer cells. J Pharm Pharm Sci 22: 281-291, 2019.
56. Muroi M and Tanamoto K: TRAF6 distinctively mediates MyD88-
and IRAK-1-induced activation of NF-kappaB. J Leukoc Biol 83:
702-707, 2008.
57. Zhang J, Macartney T, Peggie M and Cohen P: Interleukin-1 and
TRAF6-dependent activation of TAK1 in the absence of TAB2 and
TAB3. Biochem J 474: 2235-2248, 2017.
58. Jang JH, Kim H and Cho JH: Molecular cloning and functional
characterization of TRAF6 and TAK1 in rainbow trout, oncorhyn-
chus mykiss. Fish Shellsh Immunol 84: 927‑936, 2019.
59. Yu-Wei D, Li ZS, Xiong SM, Huang G, Luo YF, Huo TY, Zhou MH
and Zheng YW: Paclitaxel induces apoptosis t hrough the TAK1-JNK
activation pathway. FEBS Open Bio 10: 1655-1667, 2020.
60. Yuan Z, Liang Z, Yi J, Chen X, Li R, Wu J and Sun Z: Koumine
promotes ROS production to suppress hepatocellular carcinoma
cell proliferation via NF-κB and ERK/p38 MAPK signaling.
Biomolecules 9: 559, 2019.
61. Pan J, Jin R, Shen M, Wu R and Xu S: Acamprosate protects against
adjuvant-induced arthritis in rats via blocking the ERK/MAPK and
NF-κB signaling pathway. Inammation 41: 1194‑1199, 2018.
62. Kitanaka T, Nakano R, Kitanaka N, Kimura T, Okabayashi K,
Narita T and Sugiya H: JNK activation is essential for activation
of MEK/ERK signaling in IL-1β-induced COX-2 expression in
synovial broblasts. Sci Rep 7: 39914, 2017.
63. Rossaint J, Margraf A and Zarbock A: Role of platelets in leukocyte
recruitment and resolution of inammation. Front Immunol 9: 2712,
2018.
64. Trivedi PJ and Adams DH: Chemokines and chemokine receptors
as therapeutic targets in inammatory bowel disease; pitfalls and
promise. J Crohns Colitis 12 (Suppl 2): S641-S652, 2018.
65. Kvedaraite E, Lourda M, Ideström M, Chen P, Olsson-Åkefeldt S,
Forkel M, Gavhed D, Lindforss U, Mjösberg J, Henter JI and
Svensson M: Tissue-infiltrating neutrophils represent the main
source of IL-23 in the colon of patients with IBD. Gut 65: 1632-1641,
2016.
66. Habas K and Shang L: Alterations in intercellula r adhesion molecule
1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) in
human endothelial cells. Tissue Cell 54: 139-143, 2018.
67. Weber CR, Nalle SC, Tretiakova M, Rubin DT and Turner JR:
Claudin‑1 and claudin‑2 expression is elevated in inammatory
bowel disease and may contribute to early neoplastic transformation.
Lab Invest 88: 1110-1120, 2008.
68. Zhou J, Yu Y, Yang X, Wang Y, Song Y, Wang Q, Chen Z,
Zong S, Fan M, Meng X, et al: Berberine attenuates arthritis in
adjuvant-induced arthritic rats associated with regulating polar-
ization of macrophages through AMPK/NF‑кB pathway. Eur J
Pharmacol 852: 179-188, 2019.
69. Li H, Li XL, Zhang M, Xu H, Wang CC, Wang S and Duan RS:
Berberine ameliorates experimental autoimmune neuritis by
suppressing both cellular and humoral immunity. Scand J
Immunol 79: 12-19, 2014.
XU et al: TRANSCRIPTOME REVEALS THE EFFECT OF BERBERINE ON INTESTINAL INFLAMMATION
5180
70. Wang Q, Qi J, Hu R, Chen Y, Kijlstra A and Yang P: Effect of
berberine on proinammatory cytokine production by ARPE‑19
cells following stimulation with tumor necrosis factor-α. Invest
Ophthalmol Vis Sci 53: 2395-2402, 2012.
71. Boulangé CL, Neves AL, Chilloux J, Nicholson JK and
Dumas ME: Impact of the gut microbiota on inflammation,
obesity, and metabolic disease. Genome Med 8: 42, 2008.
72. Ali L, Schnitzler JG and Kroon J: Metabolism: The road
to inflammation and atherosclerosis. Curr Opin Lipidol 29:
474-480, 2018.
73. Xu F, Yang J, Meng B, Zheng JW, Liao Q, Chen JP and Chen XW:
The effect of berberine on ameliorating chronic inammatory
pain and depression. Zhonghua Yi Xue Za Zhi 98: 1103-1108,
2018 (In Chinese).
74. Da Silva MS, Bigo C, Barbier O and Rudkowska I: Whey protein
hydrolysate and branched-chain amino acids downregulate
inammation‑related genes in vascular endothelial cells. Nutr
Res 38: 43-51, 2017.
75. Chen YX, Gao QY, Zou TH, Wang BM, Liu SD, Sheng JQ,
Ren JL, Zou XP, Liu ZJ, Song YY, et al: Berberine versus
placebo for the prevention of recurrence of colorectal adenoma:
A multicentre, double-blinded, randomised controlled study.
Lancet Gastroenterol Hepatol 5: 267-275, 2020.
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