Key words
endometriosis - mouse syngeneic model - DNA microarray - patients - differentially
expressed genes - biological process
Introduction
Endometriosis is one of the most common gynecological diseases in women of
reproductive age, and it is diagnosed in about 5% to 10% of women
during their reproductive years, which is approximately 176 million women in the
world [1]. Endometriosis is defined as the
presence of endometrial-like lesions outside the uterus, primarily in the
peritoneum, ovaries, bowel, uterosacral ligaments, and fallopian tubes, which has
a
great impact on quality of life [2]. The
combined oral contraceptive pill and progestogens are widely used as therapies for
endometriosis [3]. Although they are effective
for some symptoms of endometriosis such as pain, they are not a complete therapy;
some patients show recurrence of the disease after withdrawal of the therapy and
one-third of patients are non-responders due to progesterone resistance [4]. Thus, new therapeutic options which have a
mechanism of action that is different from that of hormonal drugs and which act on
endometriotic lesions are desirable for the treatment of endometriosis.
To achieve this goal, the extrapolation of information from animal models to humans
is essential; however, extrapolation is complicated because rodents do not develop
endometriosis spontaneously [5]. Among the
several rodent models available, the syngeneic mouse model is often used because it
is considered to mimic retrograde menstruation [6], which is one of the main causes of the development of endometriosis
[7]. However, few studies have
comprehensively compared the biological processes of endometriosis in patients and
in the model, and the usefulness of this animal model in the interpretation of the
pathophysiology of endometriosis in humans is not yet fully understood.
In recent years, transcriptome analysis has been one of the technologies most
utilized to study human diseases at the gene expression level, and it has
contributed to the development of data integration approaches to discover molecular
biomarkers in human pathologies and targets for new drugs [8]. Therefore, in the present study, we
employed a syngeneic mouse endometriosis model and used transcriptome analysis to
investigate the differentially expressed genes and the biological processes common
to the model and endometriosis patients.
Materials and Methods
Animals
Seven-week-old female BALB/cCrSlc mice (n=65) were purchased from
Japan SLC Inc. (Hamamatsu, Japan). The mice were housed under conditions of
controlled temperature (20–26°C), humidity
(35–75%), and lighting (12-h light/dark cycle) with
water and food ad libitum. The study was conducted in compliance with the
Internal Regulations on Animal Experiments at Nippon Shinyaku Co., Ltd. (Kyoto,
Japan), which are based on the Law for the Humane Treatment and Management of
Animals (Law No. 105, October 1, 1973).
Ovariectomy and mouse model of endometriosis
Eight-week-old mice were anesthetized with Isoflurane Inhalation Solution
[Pfizer] (Mylan Inc., Canonsburg, Pennsylvania, USA). The mice were
ovariectomized through bilateral paravertebral incisions, and the muscular and
skin incisions were closed with 6–0 black silk suture. Butorphanol
tartrate (1 mg/kg; Fujifilm Wako Pure Chemical Co., Osaka,
Japan) and ampicillin sodium (100 mg/kg; Viccillin; Meiji Seika
Pharma Co., Ltd., Tokyo, Japan) were administered subcutaneously. At the end of
the procedure, estradiol valerate in sesame oil
(2 μg/animal) was administered intramuscularly every
week to all mice. The day of ovariectomy was designated as day 0. On day 7, the
mice were divided into three groups by their body weight: 10 mice in the sham
group, 14 mice in the donor group, and 28 mice in the recipient group. To
construct the syngeneic mouse endometriosis model, uterine tissues from the
donor mice were harvested and minced into small cell aggregates in Medium 199
with Hanks’ Balanced Salts (Thermo Fisher Scientific, Inc., Waltham,
Massachusetts, USA) supplemented with penicillin-streptomycin mixed solution
(Nacalai Tesque Inc., Kyoto, Japan), then equal volumes of uterine cell
suspension were transferred into the peritoneal cavities of the recipient mice
at a ratio of one donor to two recipients. For the sham group, the same volume
of Medium 199 with Hanks’ Balanced Salts was injected into the
peritoneal cavities of the mice. To reduce the local surgical response to
trauma, we incised the upper right side of mice and transferred the uterine cell
suspension into their lower left peritoneal cavities through the indwelling
needle. The wounds of the mice were closed with 6–0 black silk suture
and bupivacaine hydrochloride hydrate (2.5 mg/kg; Marcaine
Injection; Aspen Japan Co., Ltd., Tokyo, Japan) and ampicillin sodium
(100 mg/kg) were administered subcutaneously. On day 35, the
recipient mice were euthanized and all ectopic cysts and uterine tissues were
carefully and exclusively removed from each mouse with a small scissors and
forceps, infused with RNAlater solution (Thermo Fisher Scientific, Inc.) and
stored at −80°C for analysis of gene expression.
Microarray analysis
Total RNA were isolated from the mouse ectopic cystic tissue and eutopic uterus
using an RNeasy Lipid Tissue Mini Kit (Qiagen Inc., Hilden, Germany)
(n=5 animals per group). The quality and concentration of the RNA was
checked using an Agilent 2100 bioanalyzer. The RNA Integrity Number (RIN) was
used to evaluate RNA integrity and all samples used for the microarray analysis
had RIN ≥7.0. Purified RNA was labeled by using the GeneChip WT Plus
Reagent Kit (Thermo Fisher Scientific, Inc.), then hybridized to a Clariom S
Mouse Array (Thermo Fisher Scientific, Inc.) according to the
manufacturer’s instructions. Experiments from RNA isolation to
microarray analysis were conducted at Filgen, Inc. (Nagoya, Japan). Briefly, CEL
files were processed using Affymetrix Expression Console software (Thermo Fisher
Scientific, Inc.) and subjected to normalization using the Signal Space
Transformation-Robust Multiarray Analysis (SST-RMA) method for the following
analysis. The number of probes detected was 22,206 and genes whose expression
changed at least two-fold with p<0.05 (Student’s t-test) in the
ectopic cystic tissue compared to the eutopic tissue in the syngeneic
endometriosis mouse model or in the eutopic tissue in the model compared to the
sham group were considered to be differentially expressed. Gene ontology (GO)
analysis was conducted on the significantly differentially expressed genes
(DEGs) using the Database for Annotation, Visualization and Integrated Discovery
[9] (DAVID; Laboratory of Human
Retrovirology and Immunoinformatics). GO terms for biological processes with
p<0.05 (Fisher’s exact test with the Benjamini-Hochberg
multiple-testing correction) were considered significant. The datasets are
available from the National Center for Biotechnology Information/Gene
Expression Omnibus, and can be accessed with GSE190209.
Endometriosis patient data collection
The BaseSpace Correlation Engine (Illumina, Inc., San Diego, California, USA)
bioinformatics database was used to investigate the microarray gene expression
profiles of the endometriosis patients, in which data were reanalyzed as
determined by NextBio analysis [10]. We
found three datasets (GSE5108 [11],
GSE7305 [12] and GSE11691 [13]) in which the gene expression in
ectopic tissue is compared to that in eutopic tissue from the same patients.
Analysis of DEGs from patient datasets
The files from the three datasets were individually processed and normalized
according to the BaseSpace Correlation Engine platform, and genes whose
expression changed in ectopic tissue at least two-fold compared to eutopic
tissue with p<0.05 were considered to be the DEGs of each dataset. The
genes which showed the same expression pattern (up-regulated or down-regulated)
in at least two datasets were defined as the DEGs of the endometriosis patients.
GO analysis was conducted on the DEGs of patients using DAVID. GO terms for
biological processes with p<0.05 (Fisher’s exact test with the
Benjamini-Hochberg multiple-testing correction) were considered significant.
Comparison of data between the syngeneic mouse endometriosis model and
patients
The data for the GO analysis of the syngeneic mouse endometriosis model were
combined with those of the patients, then GO terms common to them were
identified using TIBCO Spotfire data analysis software (TIBCO Software Inc.,
Palo Alto, California, USA). The DEGs common to the model and patients were
identified using the BaseSpace Correlation Engine. To investigate the
relationship between each common DEG and endometriosis, PubMed (National Center
for Biotechnology Information) was searched for each common DEG along with the
terms “endometriosis” or “endometriosis” and
“development”. Studies on genes which were not shown to be
associated with endometriosis in patients (e. g., studies in animal
models only or on endometriosis-associated ovarian carcinoma) were excluded.
Results
DEGs in the syngeneic mouse endometriosis model
We used DNA microarray analysis to identify the changes in gene expression in the
syngeneic mouse endometriosis model. Seventy-seven out of 22,206 genes were
differentially expressed in the eutopic uterus of the model compared to that of
sham-operated mice, comprising 54 up-regulated and 23 down-regulated genes,
hereinafter referred to as the DEGs in the eutopic uterus ([Fig. 1a]). We then investigated the DEGs
in the ectopic cystic tissue of the model mice compared to those in their
eutopic uteri. We identified 1,154 out of 22,206 genes as DEGs, comprising 742
up-regulated and 412 down-regulated genes, and these are hereinafter referred to
as the DEGs in ectopic tissue ([Fig.
1b]). These results show that the expression of some genes was different
between the eutopic and ectopic tissues of the model mice.
Fig. 1 Results of DNA microarray analysis in the mouse
endometriosis model. The volcano plots represent the DEGs between
(a) the eutopic uterus in the sham mice and in the syngeneic
endometriosis mouse model or (b) the eutopic uterus and ectopic
tissue in the model. DEGs satisfy the criteria log2(fold
change)>1 or<−1 and p<0.05
(Student’s t-test). Significantly differentially expressed genes
are shown as black dots. DEGs, differentially expressed genes.
DEGs in the endometriosis patients of three datasets from NCBI GEO
We identified DEGs in the endometriosis patients using three datasets from NCBI
GEO in which the gene expression between eutopic and ectopic lesions from the
endometriosis patients was compared using microarray analysis. We identified
2633 genes in GSE5108, 3787 in GSE7305, and 494 in GSE11691. Of these, 950 genes
showed the same expression pattern in at least two datasets and were defined as
the DEGs common to the patients. They comprised 530 up-regulated and 420
down-regulated genes ([Fig. 2]).
Fig. 2 Identification of DEGs in endometriosis patients. Datasets
(GSE5108, GSE7305 and GSE11691) from the NCBI GEO database in which the
gene expression of ectopic and ectopic tissue is compared were used for
analysis. The DEGs of each dataset were displayed in Venn diagrams and
the overlapping DEGs, that is, DEGs which showed the same expression
pattern (up-regulated or down-regulated) in at least two datasets, were
defined as common DEGs in the endometriosis patients.
GO analysis of DEGs in the mouse model and endometriosis patients
To find biological processes associated with the DEGs, we used gene ontology (GO)
analysis. We found that DEGs in the eutopic uterus of the model mice represented
the enrichment of two biological processes, the response to lipopolysaccharide
and neutrophil chemotaxis ([Table 1]).
The DEGs in the ectopic tissue of the model mice represented the enrichment of
75 biological processes, including muscle contraction, cell adhesion, response
to hypoxia, and the inflammatory response (Supplementary Table 1). The
DEGs in the patients represented the enrichment of 28 biological processes,
including extracellular matrix organization, cell adhesion, and the inflammatory
response (Supplementary Table 2). We then matched GO terms which were
enriched both in the ectopic tissue of the model mice and in the patients, and
found that 12 biological processes were common to them ([Table 2] and [Fig. 3]), including cell adhesion, the
inflammatory response, the response to mechanical stimulus, cell proliferation
and extracellular matrix organization. This result suggests that these
biological processes are important in both the model and patients.
Fig. 3 Identification of biological processes common to the
syngeneic mouse endometriosis model and endometriosis patients. Gene
ontology (GO) analysis was conducted using DEGs in the ectopic tissue of
the model mice and the patients, and biological processes that were
enriched in both were identified.
Table 1 The significantly enriched biological processes
associated with DEGs in the eutopic uterus of the mouse
model
|
GO Term
|
|
Count
|
p-value
|
|
GO:0032496
|
response to lipopolysaccharide
|
7
|
0.03
|
|
GO:0030593
|
neutrophil chemotaxis
|
5
|
0.03
|
Table 2 GO terms common to the syngeneic mouse
endometriosis model and endometriosis patients
|
GO term
|
|
Mouse model
|
Endometriosis patients
|
|
|
Gene Count
|
p-value
|
Gene Count
|
p-value
|
|
GO:0007155
|
cell adhesion
|
74
|
3.2.E-11
|
60
|
7.0.E-08
|
|
GO:0006954
|
inflammatory response
|
51
|
6.7.E-07
|
46
|
1.1.E-04
|
|
GO:0009612
|
response to mechanical stimulus
|
17
|
9.9.E-05
|
12
|
3.0.E-02
|
|
GO:0008285
|
negative regulation of cell proliferation
|
47
|
3.6.E-04
|
38
|
3.8.E-02
|
|
GO:0030198
|
extracellular matrix organization
|
22
|
3.8.E-04
|
37
|
5.1.E-08
|
|
GO:0043627
|
response to estrogen
|
17
|
7.7.E-04
|
12
|
5.0.E-02
|
|
GO:0001525
|
angiogenesis
|
33
|
1.0.E-03
|
29
|
3.1.E-03
|
|
GO:0045766
|
positive regulation of angiogenesis
|
21
|
1.8.E-03
|
20
|
2.2.E-03
|
|
GO:0007568
|
aging
|
24
|
1.1.E-02
|
21
|
3.8.E-02
|
|
GO:0006955
|
immune response
|
32
|
1.4.E-02
|
45
|
2.2.E-03
|
|
GO:0070098
|
chemokine-mediated signaling pathway
|
12
|
1.8.E-02
|
13
|
3.5.E-02
|
|
GO:0048247
|
lymphocyte chemotaxis
|
9
|
2.6.E-02
|
8
|
5.0.E-02
|
DEGs common to the syngeneic mouse endometriosis model and endometriosis
patients
To identify gene-expression changes common to the model and the patients, we
compared the DEGs between them. We found that they shared 195 DEGs, of which 154
showed the same expression pattern (that is, 115 genes were up-regulated and 39
were down-regulated in both the model and the patients; [Table 3] and [Fig. 4]). We defined these 154 genes as
the DEGs common to the model and the patients. We then explored the gene
annotations of the common DEGs, and found that some of them were annotated by GO
terms which were enriched in both the model and patients ([Table 4]).
Fig. 4 Identification of DEGs common to the syngeneic mouse
endometriosis model and endometriosis patients. DEGs in the ectopic
tissue of the model mice were compared to those in the patients. The
DEGs of each dataset were displayed in Venn diagrams and the overlapping
DEGs identified by selecting genes which showed the same expression
pattern (up-regulated or down-regulated).
Table 3 DEGs common to the syngeneic mouse endometriosis
model and endometriosis patients
|
Gene
|
Description
|
Fold changein model
|
Fold change in patients (average of 3 datasets)
|
|
up-regulated genes
|
|
|
|
|
Hp
|
Haptoglobin
|
468.70
|
9.18
|
|
Cfd
|
complement factor D (adipsin)
|
405.34
|
6.15
|
|
Fabp4
|
fatty acid binding protein 4, adipocyte
|
280.26
|
30.81
|
|
Hspb6
|
heat shock protein, alpha-crystallin-related, B6
|
144.70
|
2.31
|
|
Serpina3n
|
serine (or cysteine) peptidase inhibitor, clade A, member
3 N
|
58.06
|
5.74
|
|
Cryab
|
crystallin, alpha B
|
42.87
|
3.25
|
|
Hsd11b1
|
hydroxysteroid 11-beta dehydrogenase 1
|
41.74
|
20.65
|
|
Gpnmb
|
glycoprotein (transmembrane) nmb
|
39.08
|
3.53
|
|
Ldb3
|
LIM domain binding 3
|
33.46
|
3.86
|
|
Cpxm2
|
carboxypeptidase X 2 (M14 family)
|
31.13
|
16.65
|
|
Rgs16
|
regulator of G-protein signaling 16
|
30.34
|
2.62
|
|
Serpine2
|
serine (or cysteine) peptidase inhibitor, clade E, member
2
|
22.80
|
18.67
|
|
Thbs2
|
thrombospondin 2
|
20.39
|
4.11
|
|
Lrrc2
|
leucine rich repeat containing 2
|
17.94
|
4.20
|
|
Filip1l
|
filamin A interacting protein 1-like
|
17.62
|
3.99
|
|
Col12a1
|
collagen, type XII, alpha 1
|
16.63
|
5.26
|
|
Fmod
|
Fibromodulin
|
15.29
|
2.75
|
|
Thbs4
|
thrombospondin 4
|
12.82
|
3.89
|
|
Mgp
|
matrix Gla protein
|
12.49
|
4.65
|
|
Timp1
|
tissue inhibitor of metalloproteinase 1
|
12.08
|
5.25
|
|
Thbs1
|
thrombospondin 1
|
11.39
|
6.86
|
|
C1qtnf7
|
C1q and tumor necrosis factor related protein 7
|
10.29
|
2.27
|
|
Itm2a
|
integral membrane protein 2 A
|
9.54
|
7.11
|
|
Sfrp2
|
secreted frizzled-related protein 2
|
8.96
|
21.75
|
|
Il7r
|
interleukin 7 receptor
|
8.48
|
5.82
|
|
Slit3
|
slit homolog 3 (Drosophila)
|
8.08
|
2.86
|
|
Itgbl1
|
integrin, beta-like 1
|
7.92
|
4.05
|
|
Angptl1
|
angiopoietin-like 1
|
7.46
|
13.75
|
|
Sulf1
|
sulfatase 1
|
7.43
|
3.22
|
|
Bgn
|
Biglycan
|
6.91
|
3.43
|
|
Ghr
|
growth hormone receptor
|
6.84
|
2.79
|
|
Inhba
|
inhibin beta-A
|
6.45
|
8.09
|
|
Cd163
|
CD163 antigen
|
6.37
|
5.35
|
|
Chl1
|
cell adhesion molecule with homology to L1CAM
|
5.96
|
36.95
|
|
Pdgfrl
|
platelet-derived growth factor receptor-like
|
5.72
|
3.80
|
|
Fhl5
|
four and a half LIM domains 5
|
5.64
|
2.58
|
|
Olfml1
|
olfactomedin-like 1
|
5.54
|
2.55
|
|
Nupr1
|
nuclear protein 1
|
5.43
|
2.37
|
|
Rcan2
|
regulator of calcineurin 2
|
5.20
|
8.91
|
|
Frzb
|
frizzled-related protein
|
5.04
|
5.21
|
|
Scn7a
|
sodium channel, voltage-gated, type VII, alpha
|
4.81
|
37.20
|
|
Lyz2
|
lysozyme 2
|
4.75
|
4.23
|
|
Vgll3
|
vestigial like 3 (Drosophila)
|
4.62
|
3.04
|
|
Lhfp
|
lipoma HMGIC fusion partner
|
4.53
|
3.59
|
|
Lbh
|
limb-bud and heart
|
4.52
|
2.50
|
|
Wisp2
|
WNT1 inducible signaling pathway protein 2
|
4.52
|
13.38
|
|
Gfpt2
|
glutamine fructose-6-phosphate transaminase 2
|
4.37
|
2.24
|
|
Msr1
|
macrophage scavenger receptor 1
|
4.36
|
3.90
|
|
Ctss
|
cathepsin S
|
4.01
|
2.59
|
|
C4a
|
complement component 4 A (Rodgers blood group)
|
3.97
|
7.01
|
|
Rgs5
|
regulator of G-protein signaling 5
|
3.85
|
3.40
|
|
Dpysl3
|
dihydropyrimidinase-like 3
|
3.84
|
8.99
|
|
Prelp
|
proline arginine-rich end leucine-rich repeat
|
3.80
|
7.90
|
|
Itgb2
|
integrin beta 2
|
3.65
|
2.42
|
|
Aspn
|
aspirin
|
3.60
|
4.09
|
|
Meox2
|
mesenchyme homeobox 2
|
3.55
|
3.09
|
|
Cbs
|
cystathionine beta-synthase
|
3.53
|
2.58
|
|
Nrp2
|
neuropilin 2
|
3.47
|
8.76
|
|
Ccdc80
|
coiled-coil domain containing 80
|
3.43
|
8.69
|
|
S100a6
|
S100 calcium binding protein A6 (calcyclin)
|
3.42
|
2.22
|
|
Folr2
|
folate receptor 2 (fetal)
|
3.42
|
2.20
|
|
Kcnma1
|
potassium large conductance calcium-activated channel,
subfamily M, alpha member 1
|
3.42
|
2.55
|
|
Pdlim5
|
PDZ and LIM domain 5
|
3.36
|
2.71
|
|
Podn
|
Podocan
|
3.34
|
4.29
|
|
Plxdc2
|
plexin domain containing 2
|
3.32
|
2.78
|
|
Steap4
|
STEAP family member 4
|
3.32
|
4.67
|
|
Ltbp2
|
latent transforming growth factor beta binding protein 2
|
3.08
|
6.01
|
|
Spsb1
|
splA/ryanodine receptor domain and SOCS box
containing 1
|
3.06
|
2.45
|
|
Eltd1
|
EGF, latrophilin seven transmembrane domain containing 1
|
2.99
|
2.30
|
|
Sytl2
|
synaptotagmin-like 2
|
2.96
|
5.78
|
|
Gpx3
|
glutathione peroxidase 3
|
2.91
|
10.59
|
|
Hmox1
|
heme oxygenase (decycling) 1
|
2.90
|
4.67
|
|
Chrdl1
|
chordin-like 1
|
2.88
|
5.43
|
|
Ncf4
|
neutrophil cytosolic factor 4
|
2.87
|
3.66
|
|
Loxl1
|
lysyl oxidase-like 1
|
2.85
|
2.76
|
|
Rarres1
|
retinoic acid receptor responder (tazarotene induced) 1
|
2.78
|
7.20
|
|
Rerg
|
RAS-like, estrogen-regulated, growth-inhibitor
|
2.75
|
5.45
|
|
Sep4
|
septin 4
|
2.75
|
3.94
|
|
Pdgfd
|
platelet-derived growth factor, D polypeptide
|
2.71
|
5.77
|
|
Col14a1
|
collagen, type XIV, alpha 1
|
2.69
|
3.54
|
|
Nfasc
|
Neurofascin
|
2.68
|
14.96
|
|
Tspan7
|
tetraspanin 7
|
2.67
|
2.67
|
|
Colec12
|
collectin sub-family member 12
|
2.66
|
3.25
|
|
Igsf6
|
immunoglobulin superfamily, member 6
|
2.65
|
2.96
|
|
Cdh5
|
cadherin 5
|
2.64
|
2.47
|
|
Plvap
|
plasmalemma vesicle associated protein
|
2.57
|
2.96
|
|
Clu
|
Clusterin
|
2.55
|
8.12
|
|
Fry
|
furry homolog (Drosophila)
|
2.55
|
3.56
|
|
Chi3l1
|
chitinase 3-like 1
|
2.55
|
9.68
|
|
Fcgr3
|
Fc receptor, IgG, low affinity III
|
2.54
|
5.88
|
|
Itga7
|
integrin alpha 7
|
2.53
|
3.01
|
|
Man1c1
|
mannosidase, alpha, class 1 C, member 1
|
2.52
|
3.40
|
|
Dkk3
|
dickkopf homolog 3 (Xenopus laevis)
|
2.51
|
3.51
|
|
Tril
|
TLR4 interactor with leucine-rich repeats
|
2.50
|
3.49
|
|
Pros1
|
protein S (alpha)
|
2.48
|
6.98
|
|
Fcgr2b
|
Fc receptor, IgG, low affinity IIb
|
2.44
|
3.29
|
|
Jam2
|
junction adhesion molecule 2
|
2.44
|
2.92
|
|
Ccr1
|
chemokine (C-C motif) receptor 1
|
2.42
|
2.48
|
|
Grk5
|
G protein-coupled receptor kinase 5
|
2.26
|
2.93
|
|
Pde1a
|
phosphodiesterase 1 A, calmodulin-dependent
|
2.26
|
3.38
|
|
Npl
|
N-acetylneuraminate pyruvate lyase
|
2.25
|
4.02
|
|
Ptprb
|
protein tyrosine phosphatase, receptor type, B
|
2.25
|
2.54
|
|
Serping1
|
serine (or cysteine) peptidase inhibitor, clade G, member
1
|
2.20
|
5.47
|
|
Gpr116
|
G protein-coupled receptor 116
|
2.14
|
3.21
|
|
Nr4a1
|
nuclear receptor subfamily 4, group A, member 1
|
2.13
|
2.31
|
|
Fst
|
Follistatin
|
2.11
|
6.28
|
|
Cpa3
|
carboxypeptidase A3, mast cell
|
2.08
|
2.87
|
|
Aox1
|
aldehyde oxidase 1
|
2.08
|
17.10
|
|
Gnb4
|
guanine nucleotide binding protein (G protein), beta 4
|
2.08
|
2.36
|
|
Cd22
|
CD22 antigen
|
2.07
|
3.19
|
|
Nuak1
|
NUAK family, SNF1-like kinase, 1
|
2.05
|
3.74
|
|
Gpc6
|
glypican 6
|
2.03
|
3.29
|
|
9430020K01Rik
|
RIKEN cDNA 9430020K01 gene
|
2.02
|
3.09
|
|
C7
|
complement component 7
|
2.02
|
73.71
|
|
Laptm5
|
lysosomal-associated protein transmembrane 5
|
2.01
|
2.94
|
|
down-regulated genes
|
|
|
|
|
Hsd11b2
|
hydroxysteroid 11-beta dehydrogenase 2
|
−8.06
|
−5.61
|
|
Mogat1
|
monoacylglycerol O-acyltransferase 1
|
−7.94
|
−4.09
|
|
Kcnip4
|
Kv channel interacting protein 4
|
−6.37
|
−4.81
|
|
Gcnt3
|
glucosaminyl (N-acetyl) transferase 3, mucin type
|
−5.21
|
−2.26
|
|
Car12
|
carbonic anyhydrase 12
|
−5.21
|
−6.39
|
|
Slc15a2
|
solute carrier family 15 (H+/peptide
transporter), member 2
|
−4.27
|
−4.48
|
|
Pgbd5
|
piggyBac transposable element derived 5
|
−3.38
|
−4.45
|
|
Crabp2
|
cellular retinoic acid binding protein II
|
−3.28
|
−6.77
|
|
Mme
|
membrane metallo endopeptidase
|
−3.28
|
−4.71
|
|
Ckb
|
creatine kinase, brain
|
−3.27
|
−3.01
|
|
Krt8
|
keratin 8
|
−3.23
|
−3.75
|
|
Krt19
|
keratin 19
|
−3.19
|
−3.69
|
|
Tfcp2l1
|
transcription factor CP2-like 1
|
−3.13
|
−3.44
|
|
Tspan13
|
tetraspanin 13
|
−3.01
|
−3.43
|
|
Galnt4
|
UDP-N-acetyl-alpha-D-galactosamine:polypeptide
N-acetylgalactosaminyltransferase 4
|
−2.93
|
−11.19
|
|
Agr2
|
anterior gradient 2 (Xenopus laevis)
|
−2.85
|
−11.16
|
|
Fam174b
|
family with sequence similarity 174, member B
|
−2.71
|
−2.27
|
|
Galnt3
|
UDP-N-acetyl-alpha-D-galactosamine:polypeptide
N-acetylgalactosaminyltransferase 3
|
−2.71
|
−2.45
|
|
Rorb
|
RAR-related orphan receptor beta
|
−2.66
|
−7.46
|
|
Tspan1
|
tetraspanin 1
|
−2.53
|
−4.88
|
|
Gpsm2
|
G-protein signalling modulator 2 (AGS3-like, C. elegans)
|
−2.51
|
−2.90
|
|
Aldh1a2
|
aldehyde dehydrogenase family 1, subfamily A2
|
−2.49
|
−9.64
|
|
Prr15
|
proline rich 15
|
−2.44
|
−7.56
|
|
Rasef
|
RAS and EF hand domain containing
|
−2.36
|
−2.79
|
|
Esr1
|
estrogen receptor 1 (alpha)
|
−2.34
|
−7.53
|
|
Rev3l
|
REV3-like, catalytic subunit of DNA polymerase zeta RAD54
like (S. cerevisiae)
|
−2.34
|
−3.11
|
|
Ptn
|
Pleiotrophin
|
−2.31
|
−3.03
|
|
Tmem30b
|
transmembrane protein 30B
|
−2.29
|
−4.84
|
|
Cd24a
|
CD24a antigen
|
−2.26
|
−22.91
|
|
Qpct
|
glutaminyl-peptide cyclotransferase (glutaminyl cyclase)
|
−2.25
|
−4.16
|
|
Cndp2
|
CNDP dipeptidase 2 (metallopeptidase M20 family)
|
−2.20
|
−3.14
|
|
Wfdc2
|
WAP four-disulfide core domain 2
|
−2.20
|
−10.44
|
|
Stxbp6
|
syntaxin binding protein 6 (amisyn)
|
−2.16
|
−9.21
|
|
Rab25
|
RAB25, member RAS oncogene family
|
−2.15
|
−5.87
|
|
Llgl2
|
lethal giant larvae homolog 2 (Drosophila)
|
−2.14
|
−2.27
|
|
Npr2
|
natriuretic peptide receptor 2
|
−2.14
|
−2.80
|
|
Ppap2c
|
phosphatidic acid phosphatase type 2 C
|
−2.08
|
−4.03
|
|
Irf6
|
interferon regulatory factor 6
|
−2.04
|
−5.03
|
|
Gjb6
|
gap junction protein, beta 6
|
−2.00
|
−5.75
|
Table 4 GO terms which were enriched in DEGs common to the
syngeneic mouse endometriosis model and endometriosis
patients
|
GO term
|
genes
|
|
|
cell adhesion
|
17
|
Gpnmb, Thbs2, Col12a1, Thbs4, Thbs1, Sulf1, Chl1, Wisp2,
Itgb2, Col14a1, Nfasc, Cdh5, Itga7, Cd22, Nuak1,
9430020K01Rik, Cd24a
|
|
inflammatory response
|
6
|
Thbs1, Cd163, C4a, Chi3l1, Tril, Ccr1
|
|
response to mechanical stimulus
|
2
|
Thbs1, Chi3l1
|
|
negative regulation of cell proliferation
|
13
|
Serpine2, Sfrp2, Slit3, Inhba, Frzb, Wisp2, Podn, Hmox1,
Rerg, Cdh5, Aldh1a2, Irf6, Gjb6
|
|
extracellular matrix organization
|
1
|
Ccdc80
|
|
response to estrogen
|
5
|
Kcnma1, Hmox1, Krt19, Esr1, Cd24a
|
|
Angiogenesis
|
5
|
Meox2, Nrp2, Ccdc80, Hmox1, Ptprb
|
|
positive regulation of angiogenesis
|
5
|
Thbs1, Sfrp2, Itgb2, Hmox1, Chi3l1
|
|
Aging
|
5
|
Cryab, Timp1, Itgb2, Serping1, Gjb6
|
|
immune response
|
7
|
Thbs1, Ctss, Colec12, Fcgr2b, Ccr1, C7, Cd24a
|
|
chemokine-mediated signaling pathway
|
1
|
Ccr1
|
The roles of DEGs common to the syngeneic mouse endometriosis model and
endometriosis patients in endometriosis
To investigate possible roles played by the DEGs common to the model and the
patients, we searched for a relationship between the common DEGs and
endometriosis by using PubMed. When we searched for each gene along with the
term “endometriosis”, 52 of 154 genes came up (Supplementary
Table 3 and [Fig. 5]). When we
searched for each gene along with the terms “endometriosis” and
“development”, 23 genes came up that had some association with
endometriosis in patients ([Table
5]).
Fig. 5 Flowchart for Pubmed search. PubMed (National Center for
Biotechnology Information) was searched for each common DEG along with
the term “endometriosis” or the terms
“endometriosis” and “development”
Table 5 The DEGs common to the model and patients along
with the terms “endometriosis” and
“development” found by searching
PubMed
|
Gene
|
Number of publications
|
Reference lists
|
|
up-regulated genes
|
|
|
|
Hp
|
2
|
Piva M et al., Glycoconj J. 2002 Jan;19(1):33–41.
Sharpe-Timms KL et al., Hum Reprod. 2000
Oct;15(10):2180–5.
|
|
Hsd11b1
|
1
|
Zhen Lin et al., J Food Biochem. 2021 May;45(5):e13717.
|
|
Timp1
|
6
|
Luddi A et al., Int J Mol Sci. 2020 Apr
18;21(8):2840.Szymanowski K et al.,Ann Agric Environ Med.
2016 Dec 23;23(4):649–653. Stilley JA et al., Biol
Reprod. 2010 Aug 1;83(2):185–94. Collette T et
al.,Hum Reprod. 2006 Dec;21(12):3059–67. Li Y et
al., Zhonghua Fu Chan Ke Za Zhi. 2006
Jan;41(1):30–3. Collette T et al., Hum Reprod. 2004
Jun;19(6):1257–64.
|
|
Thbs1
|
3
|
Liu Y et al., Am J Reprod Immunol. 2020 Jun;83(6):e13236.
Gilabert-Estellés J et al., Hum Reprod. 2007
Aug;22(8):2120–7. Tan XJ et al., Fertil Steril. 2002
Jul;78(1):148–53.
|
|
Slit3
|
1
|
Greaves E et al., Endocrinology. 2014
Oct;155(10):4015–26.
|
|
Inhba
|
1
|
Lin J et al., Mol Hum Reprod. 2011
Oct;17(10):605–11.
|
|
Cd163
|
3
|
Kusunoki M et al., Med Mol Morphol. 2021
Jun;54(2):122–132. Krasnyi AM et al., Biomed Khim.
2019 Aug;65(5):432–436. Itoh F et al., Fertil
Steril. 2013 May;99(6):1705–13.
|
|
Chl1
|
2
|
Jiang L et al., Int J Immunopathol Pharmacol. 2020
Jan-Dec;34:2058738420976309. Zhang C et al., Eur J Obstet
Gynecol Reprod Biol. 2019 May;236:177–182.
|
|
Prelp
|
1
|
Araujo FM et al., Braz J Med Biol Res. 2017 Jul
3;50(7):e5782.
|
|
Itgb2
|
1
|
Sundqvist J et al., Hum Reprod. 2012
Sep;27(9):2737–46.
|
|
S100a6
|
1
|
Peng Y et al., Gynecol Endocrinol. 2018
Sep;34(9):815–820.
|
|
Gpx3
|
1
|
Mirza Z et al., Diagnostics (Basel) . 2020 Jun
19;10(6):416.
|
|
Hmox1
|
2
|
Van LA et al., Fertil Steril. 2002 Mar;77(3):561–70.
Imanaka S et al., Arch Med Res. 2021
Aug;52(6):641–647.
|
|
Fcgr3
|
1
|
Mei J et al., Autophagy. 2018;14(8):1376–1397.
|
|
Ccr1
|
3
|
Li T et al., Biomed Pharmacother. 2020 Sep;129:110476.
Trummer D et al., Acta Obstet Gynecol Scand. 2017
Jun;96(6):694–701. Kyama CM et al., Curr Med Chem.
2008;15(10):1006–17.
|
|
Nr4a1
|
1
|
Qingdong Z et al., Cell Physiol Biochem.
2018;45(3):1172–1190.
|
|
Fst
|
2
|
Kimber-Trojnar Ż et al., J Clin Med. 2021 Jun
23;10(13):2762. Luisi S et al., Womens Health (Lond). 2015
Aug;11(5):603–10.
|
|
down-regulated genes
|
|
|
|
Crabp2
|
1
|
Sokalska A et al., J Clin Endocrinol Metab. 2013
Mar;98(3):E463–71.
|
|
Krt19
|
1
|
Konrad L et al., Reprod Sci. 2019
Jan;26(1):49–59.
|
|
Aldh1a2
|
1
|
Jiang Y et al., J Endocrinol. 2018 Mar;236(3):R169-R188.
|
|
Esr1
|
18
|
Wang J etal., Clin Lab. 2020 Aug 1;66(8). Huang ZX et al., J
Cell Mol Med. 2020 Sep;24(18):10693–10704. Gibson DA
et al., J Endocrinol. 2020 Sep;246(3):R75-R93. Chantalat E
et al., Int J Mol Sci. 2020 Apr 17;21(8):2815. Tang ZR et
al., Cells. 2019 Sep 21;8(10):1123. Yilmaz BD et al., Hum
Reprod Update. 2019 Jul 1;25(4):473–485.
Osiński M et al., Ginekol Pol.
2018;89(3):125–134. Sapkota Y et al., Nat Commun.
2017 May 24;8:15539. Hamilton KJ et al., Curr Top Dev Biol.
2017;125:109–146. Xiong W et al., Reproduction. 2015
Dec;150(6):507–16 Zhang Q et al., Gynecol Obstet
Invest. 2015;80(3):187–92. Huang PC et al., Environ
Sci Pollut Res Int. 2014 Dec;21(24):13964–73. Wang W
et al., Reprod Biomed Online. 2013 Jan;26(1):93–8 Li
Y et al., Gene. 2012 Oct 15;508(1):41–8. Veillat V
et al., Am J Pathol. 2012 Sep;181(3):917–27.
Matsuzaka Y et al., Environ Health Prev Med. 2012
Sep;17(5):423–8. Athanasios F et al., Arch Gynecol
Obstet. 2012 Apr;285(4):1001–7. Smuc T et al., Mol
Cell Endocrinol. 2009 Mar
25;301(1–2):59–64.
|
|
Cd24a
|
1
|
Sundqvist J et al., Hum Reprod. 2012
Sep;27(9):2737–46.
|
|
Wfdc2
|
1
|
Chen T et al., J Clin Lab Anal. 2021 Sep;35(9):e23947.
|
Discussion
In the present study, we found that biological processes including cell adhesion,
the
inflammatory response, the response to mechanical stimulus, cell proliferation,
extracellular matrix organization (ECM), and the estrogen response were enriched in
both the model and patients. We found that thrombospondin 1 (Thbs1), tissue
inhibitor of metalloproteinase 1 (Timp1), and cell adhesion molecule with homology
to L1CAM (Chl1) were up-regulated in both the model and patients. These genes are
known to play a role in cell adhesion and/or ECM organization, biological
processes important for the attachment and invasion of ectopic cells in tissues
[14]
[15]
[16]. Thus, these genes might be
critical for the development of endometriosis via cell attachment and invasion in
both model and patients. The inflammatory and immune responses are also critical to
the development of endometriosis. Single-cell analysis has shown that T cells in
endometriosis are less activated, cytotoxic T cell populations and the proportion
of
natural killer cells in endometriosis lesions are decreased, and the ratio of
monocytes to macrophages is increased in endometriosis cysts whose main population
highly expresses CD206 and CD163, which have been described as M2 macrophage markers
[17]. In the present study, the gene
expression of haptoglobin and CD163 was upregulated in both the model and patients.
Haptoglobin is an acidic glycoprotein and ligand of CD163, which is a surface
hemoglobin-haptoglobin scavenger receptor, and is related to the development of
endometriosis [18]. These results suggest that
M2 macrophages might be critical for the development of endometriosis in both model
and patients. Furthermore, endometriosis is considered to be an estrogen-dependent
disease. Previous studies have shown that the aberrant expression of hormone
receptors in endometriosis lesions, including high estrogen receptor 2 (Esr2) to
Esr1 ratios, is related progesterone resistance [19]. In our study, the gene expression of Esr1 was decreased in both the
model and patients, suggesting that the estrogen response is also important in the
pathogenesis of this model, despite the fact that the rodent model does not exhibit
menstruation. Thus, this model partly reflects the pathophysiology of endometriosis
that occurs in humans as mentioned above, and it might be useful for evaluating the
efficacy of new therapeutic agents targeting biological processes that include cell
adhesion and ECM remodeling, inflammatory and immune responses, cell proliferation,
angiogenesis, and the estrogen response.
We found for the first time that gene expression in the eutopic uterus was changed
in
the model, and the biological processes associated with the genes whose expression
was changed were response to lipopolysaccharide and neutrophil chemotaxis. Previous
work has shown that the expression of lipopolysaccharide in the endometrium of
endometriosis patients is increased compared to that in healthy controls [20]. These findings suggest that the model
reflects the environment not only in ectopic lesions but also in the eutopic
endometrium of endometriosis patients.
In addition to this model, immunocompromised models, in which human endometrial
tissue is injected into mice, are useful for examining the multiple cellular
pathways associated with the development of human endometriosis. However,
immunocompromised models may not mimic the inflammatory or immune response of
endometriosis patients because of the lack of a fully competent immune system in
such mice [21]. The surgical immunocompetent
model reflects the inflammation response, cell proliferation and the estrogen
response of patients, yet it may not mimic early events in the development of
endometriosis such as retrograde menstruation due to the surgical induction of
ectopic growth [21]. There is reported to be
no change in the levels of cytokeratin or E-cadherin in the epithelial cells of
ectopic endometrium, or in the excessive collagen deposition or alpha-SMA positive
myofibroblasts in the ectopic endometrium of the surgical mouse endometriosis model
[22]. In the present study, the expression
of genes related to the inflammatory or immune response and ECM remodeling was
changed in the syngeneic mouse endometriosis model, indicating that this model may
be distinct from other models.
A limitation of our study is that we did microarray analysis of whole tissues at a
specific time point. The model was found not to reflect some biological process in
humans, such as endopeptidase activity and platelet degranulation, at least under
the present experimental conditions. However, since the level of gene expression
would be expected to change with time after construction of the model, or according
to the estrous cycle or the component cells, spatiotemporal single-cell RNA
sequencing should be more effective for future study. To obtain data on gene
expression in endometriosis patients, we used the gene expression data of
endometriosis patients from three datasets in which the gene expression in ectopic
tissue is compared to that in eutopic tissue, and reanalyzed them in order to unify
the analysis method between the patient datasets. However, similar data would have
been reported consecutively, so we should also analyze those new data to increase
the sample size. Furthermore, in the future we should confirm the relationship
between disease severity and the gene expression of key molecules which seem to be
important for the development of the disease. Additionally, it is not clear whether
the DEGs common to the model and patients are the cause or the result of the
pathogenesis of endometriosis. To resolve this issue, experiments using a suppressor
or initiator for each gene are necessary. On the basis of the DEGs identified in
this study, further work would be expected to clarify molecular mechanisms
underlying the pathogenesis of endometriosis, which may lead to the identification
of new biomarkers and/or treatment targets for this disease.
