please see the attached document.
pjurrel
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INTRODUCTION
Major depressive disorder (MDD) is viewed as a major
public health problem globally. MDD has a substantial
impact on society and individuals, such as increasing
economic burden and decreasing labor productivity
[1–3]. At a global level, more than 300 million people
are estimated to suffer from MDD, which is equivalent
to 4.4% of the world’s population [4]. However, the
pathogenesis of MDD is still unclear. Some theories
have been developed to explain the biological
mechanisms of MDD, such as neurotrophic alterations
www.aging-us.com AGING 2020, Vol. 12, No. 3
Research Paper
Age-specific differential changes on gut microbiota composition in patients with major depressive disorder
Jian-Jun Chen1,2,*,#, Sirong He3,*, Liang Fang2,4,*, Bin Wang1, Shun-Jie Bai5, Jing Xie6, Chan-Juan Zhou7, Wei Wang8, Peng Xie4,7,8,# 1Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China 2Chongqing Key Laboratory of Cerebral Vascular Disease Research, Chongqing Medical University, Chongqing 400016, China 3Department of Immunology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China 4Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, China 5Department of Laboratory, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China 6Department of Endocrinology and Nephrology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400014, China 7NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, Chongqing Medical University, Chongqing 400016, China 8Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China *Equal contribution #Co-senior authors
Correspondence to: Peng Xie, Jian-Jun Chen; email: [email protected], [email protected] Keywords: major depressive disorder, gut microbiota, Firmicutes, Bacteroidetes, Actinobacteria Received: November 21, 2019 Accepted: January 12, 2020 Published: February 10, 2020
Copyright: Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Emerging evidence has shown the age-related changes in gut microbiota, but few studies were conducted to explore the effects of age on the gut microbiota in patients with major depressive disorder (MDD). This study was performed to identify the age-specific differential gut microbiota in MDD patients. In total, 70 MDD patients and 71 healthy controls (HCs) were recruited and divided into two groups: young group (age 18-29 years) and middle-aged group (age 30-59 years). The 16S rRNA gene sequences were extracted from the collected fecal samples. Finally, we found that the relative abundances of Firmicutes and Bacteroidetes were significantly decreased and increased, respectively, in young MDD patients as compared with young HCs, and the relative abundances of Bacteroidetes and Actinobacteria were significantly decreased and increased, respectively, in middle-aged MDD patients as compared with middle-aged HCs. Meanwhile, six and 25 differentially abundant bacterial taxa responsible for the differences between MDD patients (young and middle-aged, respectively) and their respective HCs were identified. Our results demonstrated that there were age-specific differential changes on gut microbiota composition in patients with MDD. Our findings would provide a novel perspective to uncover the pathogenesis underlying MDD.
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and neurotransmission deficiency [5, 6]. However, none
of these theories has been universally accepted.
Therefore, there is a pressing need to identify novel
pathophysiologic mechanisms underlying this disease.
In recent years, mounting evidence has shown that gut
microbiota could play a vital role in every aspect of
physiology [7]. It is the largest and most direct external
environment of humans. Previous studies found that the
disturbance of gut microbiota had a crucial role in the
pathogenesis of many diseases [8–10]. Recent studies
reported that gut microbiota could affect the host brain
function and host behaviors through microbiota-gut-
brain axis [11, 12]. Using germ-free mice, we found that
gut microbiota could influence the gene levels in the
hippocampus of mice and lipid metabolism in the
prefrontal cortex of mice [13, 14]. Our clinical studies
demonstrated that the disturbance of gut microbiota
might be a contributory factor in the development of
MDD [15, 16].
Nowadays, emerging evidence has shown the age-
related changes in gut microbiota composition. For
example, Firmicutes is the dominant taxa during the
neonatal period, but Actinobacteria and Proteobacteria
are about to increase in three to six months [17]. While
in adults, Vemuri et al. reported that Bacteroidetes and
Firmicutes were the dominant taxa [18]. Meanwhile,
compared to younger individuals, the abundance of
Bacteroidetes is significantly higher in frailer older
individuals [19]. These results showed that there was a
close relationship between age and gut microbiota
composition. Ignoring this relationship would affect the
robust of results when exploring the mechanism of
action of gut microbiota in diseases. Therefore, to study
the relationship between gut microbiota and MDD
patients in different age groups, we recruited 52 young
subjects aged from 18 to 29 years (27 healthy controls
(HCs) and 25 MDD patients) and 89 middle-aged
subjects aged from 30 to 59 years (44 HCs and 45 MDD
patients). The main purpose of this study was to identify
the age-specific differential changes on gut microbiota
composition in MDD patients. Our results would
display the different changes of gut microbiota
composition along with age between HCs and MDD
patients.
RESULTS
Differential gut microbiota composition
As shown in Figure 1, the results of abundance-based
coverage estimator (ACE) and Chao1 showed that there
was no significant difference in OTU richness between
MDD patients (young and middle-aged, respectively)
and their respective HCs. However, the OPLS-DA
model built with young HCs and young MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 2A). The relative
abundances of Firmicutes and Bacteroidetes were
Figure 1. Comparison of alpha diversity between HCs and MDD patients. (A, B) ACE and Chao1 indexes showed no significant differences between young HCs (n=27) and young MDD patients (n=25); (C, D) ACE and Chao1 indexes showed no significant differences between middle-aged HCs (n=44) and middle-aged MDD patients (n=45).
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significantly decreased and increased, respectively, in
young MDD patients as compared with young HCs
(Figure 2B). Meanwhile, the OPLS-DA model built
with middle-aged HCs and middle-aged MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 3A). The relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
middle-aged MDD patients as compared with middle-
aged HCs (Figure 3B).
Key discriminatory OTUs
In order to find out the gut microbiota primarily
responsible for the separation between MDD patients
(young and middle-aged, respectively) and their
respective HCs, the Random Forests classifier was used.
A total of 92 OTUs responsible for the separation
between young MDD patients and young HCs were
identified (Figure 4). These OTUs were mainly assigned
to the Families of Bacteroidaceae, Clostridiaceae_1,
Figure 2. 16S rRNA gene sequencing reveals changes to microbial abundances in young MDD patients. (A) OPLS-DA model showed an obvious difference in microbial abundances between the two groups (HCs, n=27; MDD, (n=25); (B) the relative abundances of Firmicutes and Bacteroidetes were significantly changed in young MDD patients (n=25) as compared with young HCs (n=27).
Figure 3. 16S rRNA gene sequencing reveals changes to microbial abundances in middle-aged MDD patients. (A) OPLS-DA model showed an obvious difference in microbial abundances between the two groups (HCs, n=44; MDD, (n=45); (B) the relative abundances of Bacteroidetes and Actinobacteria were significantly changed in middle-aged MDD patients (n=45) as compared with middle-aged HCs (n=44).
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Coriobacteriaceae, Erysipelotrichaceae, Lachnospiraceae,
Peptostreptococcaceae and Ruminococcaceae.
Meanwhile, a total of 122 OTUs responsible for the
separation between middle-aged MDD patients and
middle-aged HCs were identified (Figure 5). These OTUs
were mainly assigned to the Families of Lachnospiraceae,
Coriobacteriaceae, Streptococcaceae, Prevotellaceae,
Bacteroidaceae, Eubacteriaceae, Actinomycetaceae,
Sutterellaceae, Acidaminococcaceae, Erysipelotrichaceae,
Ruminococcaceae, and Porphyromonadaceae.
Figure 4. Heatmap of discriminative OTUs abundances between young HCs (n=27) and young MDD patients (n=25).
Figure 5. Heatmap of discriminative OTUs abundances between middle-aged HCs (n=44) and middle-aged MDD patients (n=45).
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Differentially abundant bacterial taxa
Differentially abundant bacterial taxa responsible for
the differences between MDD patients (young and
middle-aged, respectively) and their respective HCs
were identified by the metagenomic Linear
Discriminant Analysis (LDA) Effect Size (LEfSe)
approach (LDA score>2.0 and p-value<0.05). In total,
six bacterial taxa with statistically significant and
biologically consistent differences in young MDD
patients were identified (Figure 6). Meanwhile, fifteen
bacterial taxa with statistically significant and
biologically consistent differences in middle-aged MDD
patients were identified (Figure 7). In addition, using
Figure 6. Differentially abundant features identified by LEfSe that characterize significant differences between young HCs (n=27) and young MDD patients (n=25).
Figure 7. Differentially abundant features identified by LEfSe that characterize significant differences between middle-aged HCs (n=44) and middle-aged MDD patients (n=45).
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the receiver operating characteristic (ROC) curve
analysis, we found that Clostridium_sensu_stricto,
Clostridium_XI and Clostridium_XVIII showed good
diagnostic performance (area under the curve (AUC)
>0.7) in diagnosing young MDD patients (Figure 8A–
8C). We also found that Anaerostipes, Streptococcus,
Blautia, Faecalibacterium and Roseburia showed good
diagnostic performance (AUC>0.7) in diagnosing
middle-aged MDD patients (Figure 8D–8H).
Effects of age on microbial abundances
Using the LEfSe approach, we identified four
differentially abundant bacterial taxa (the Family
level) between young HCs and middle-aged HCs
(Streptococcaceae, Coriobacteriaceae, Carnobacteriaceae
and Clostridiales_Incertae_Sedis_XIII) (Figure 9A);
we also identified six differentially abundant bacterial
taxa (the Family level) between young MDD patients
and middle-aged MDD patients (Prevotellaceae,
Acidaminococcaceae, Veillonellaceae Peptostrep-
tococcaceae, Lachnospiraceae and Ruminococcaceae)
(Figure 9B). Meanwhile, using the LEfSe approach, we
identified five differentially abundant bacterial taxa (the
Genus level) between young HCs and middle-aged HCs
(Streptococcus, Veillonella, Granulicatella, Collinsella
and Megamonas) (Figure 10A). All these bacterial
taxa were significantly decreased in middle-aged
HCs; we also identified nine differentially abundant
bacterial taxa (the Genus level) between young MDD
patients and middle-aged MDD patients (Megamonas,
Prevotella, Phascolarctobacterium, Anaerostipes,
Clostridium_XVIII, Gordonibacter, Eggerthella,
Clostridium_XI and Turicibacter) (Figure 10B).
Effects of medication on microbial abundances
To determinate the homogeneity of gut microbiota
composition between medicated and non-medicated
MDD patients, we firstly used the middle-aged HCs and
non-medicated middle-aged MDD patients to built
OPLS-DA model (Figure 11A). The results showed that
41 of the 44 middle-aged HCs and 30 of the 31 non-
medicated middle-aged MDD patients were correctly
diagnosed by the OPLS-DA model. Then, we used the
built model to predict class membership of 14
medicated middle-aged MDD patients. The T-predicted
scatter plot showed that 11 of the 14 medicated middle-
aged MDD patients were correctly predicted (Figure
11B). These finding indicated that the gut microbiota
composition of non-medicated middle-aged MDD
patients were distinct from middle-aged HCs, but not
from medicated middle-aged MDD patients.
DISCUSSION
Individuals in the different phases of life cycle (named
children, young, middle-aged and elderly) present
different biological characteristics and disease risks
[20]. Understanding the different characteristics of
patients in particular age phases could be facilitated to
prevent and treat diseases. According to the World
Health Organization reported, the prevalence rates of
depression vary by age, peaking in older adulthood. It
also occurs in children, but at a lower level compared
with older age groups. Here, we conducted this work to
investigate how the gut microbiota composition
changed in different age phases of MDD patients, and
found some age-specific differential gut microbiota in
Figure 8. Differential taxa (at the genus level) with AUC>0.7 in diagnosing MDD patients from HCs. (A–C) the diagnostic performances of three taxa in diagnosing young MDD patients (n=25) from young HCs (n=27); (D–H) the diagnostic performances of five taxa in diagnosing middle-aged MDD patients (n=45) from middle-aged HCs (n=44).
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Figure 9. 16S rRNA gene sequencing reveals changes to microbial abundances at family level (Mean±SEM). (A) the abundances of four taxonomic levels were significantly changed between young HCs (n=27) and middle-aged HCs (n=44); (B) the abundances of six taxonomic levels were significantly changed between young MDD patients (n=25) and middle-aged MDD patients (n=45).
Figure 10. 16S rRNA gene sequencing reveals changes to microbial abundances at genus level (Mean±SEM). (A) the abundances of five taxonomic levels were significantly changed between young HCs (n=27) and middle-aged HCs (n=44); (B) the abundances of nine taxonomic levels were significantly changed between young MDD patients (n=25) and middle-aged MDD patients (n=45).
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MDD patients. Our results could provide a new
perspective on exploring the pathogenesis of MDD.
Many previous studies focused on the effects of gut
microbiota on brain functions [21, 22]. However, few
studies have taken the effects of age on gut microbiota
into consideration when exploring the pathogenesis of
MDD. Our previous study found that the relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
MDD patients as compared with HCs [15]. But, in this
study, we found that the relative abundances of
Firmicutes and Bacteroidetes were significantly
decreased and increased, respectively, in young MDD
patients as compared with young HCs, and the relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
middle-aged MDD patients as compared with middle-
aged HCs. This disparity might be caused by the
different age structures. Meanwhile, only 35 key
discriminatory OTUs were significantly changed in both
young (92 key discriminatory OTUs) and middle-aged
(127 key discriminatory OTUs) MDD patients.
Moreover, the differentially abundant bacterial taxa in
young and middle-aged MDD patients were totally
different at both Family level and Genus level. These
results demonstrated that it was necessary to identify the
age-specific differential gut microbiota in patients with
MDD.
As far as we known, gut microbiota composition and its
function could be easily influenced by many factor,
such as gender, age, life experiences, dietary habit and
genetics. Mariat et al reported that the
Firmicutes/Bacteroidetes ratio of the human microbiota
could change with age [23]. Interestingly, here we
found that the relative abundance of Firmicutes was
significantly decreased in young MDD patients, but not
in middle-aged MDD patients; the relative abundance of
Bacteroidetes was significantly increased and
decreased, respectively, in young and middle-aged
MDD patients. In our previous studies, we did not
analyze the potential effects of medication on gut
microbiota composition in MDD patients [15, 16]. Here,
due to the small samples of young group, we only used
the middle-aged group to analyze the effects of
Figure 11. Assessment of gut microbiota composition in non-medicated and medicated middle-aged MDD patients. (A) middle-aged HCs (n=44) and non-medicated middle-aged MDD patients (n=31) were effectively separated by the built OPLS-DA model; (B) 14 medicated middle-aged MDD patients were correctly predicted by the model.
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medication on the gut microbiota composition. The
results showed that the medication seemed to have little
effects on gut microbiota composition in MDD patients.
However, our findings had to be cautiously interpreted
due to the relatively small samples using to analyze the
effects of medication on gut microbiota composition.
The relative abundance of genus Clostridium_XVIII
was not found to be significantly different between
MDD patients and HCs in our previous study [15].
However, in this study, we found that the relative
abundance of genus Clostridium_XVIII was
significantly decreased in young MDD patients
compared with young HCs, while increased in middle-
aged MDD patients compared with middle-aged HCs.
The reason of this disparity might be that age could
significantly affect the relative abundance of genus
Clostridium_XVIII in MDD patients, but not HCs: i)
compared to young MDD patients, the middle-aged
MDD patients had a significantly higher relative
abundance of genus Clostridium_XVIII; and ii) the
relative abundance of genus Clostridium_XVIII was
similar between young and middle-aged HCs.
Meanwhile, we found that the relative abundance of
genus Megamonas was significantly decreased in both
middle-aged HCs and middle-aged MDD patients
compared to their respective young populations. In
addition, most of differential bacterial taxa were
significantly decreased in middle-aged HCs compared
with young HCs, but only about half of differential
bacterial taxa were significantly decreased in middle-
aged MDD patients compared with young MDD
patients. Lozupone et al. reported that gut microbiota
could not only simply determine the certain host
characteristics, but also respond to signals from host via
multiple feedback loops [24]. Therefore, our results
suggested that age might have the different effects on
the gut microbiota composition of HCs and MDD
patients, and should always be considered in
investigating the relationship between MDD and gut
microbiota.
Limitations should be mentioned here. Firstly, the
number of HCs and MDD patients was relatively small,
and future works were still needed to further study and
support our results. Secondly, we only explored the age-
specific differential changes on gut microbiota
composition in patients with MDD; future studies
should further investigate the functions of these
identified differential gut microbiota using
metagenomic technology. Thirdly, all included subjects
were from the same site and ethnicity; thus, the
potential site- and ethnic-specific biases in microbial
phenotypes could not be ruled out, which might limit
the applicability of our results [25–28]. Fourthly, only
young and middle-aged groups were recruited, future
studies should recruit old-aged group and children
group to further identify the age-specific differential gut
microbiota in the different phases of life cycle. Fifthly,
we only investigated the differences in gut microbiota
between HCs and MDD patients on phylum level,
family level and genus level. Future studies were
needed to further explore the differences on other
levels, such as class level and species level. Sixthly, we
did not collect information on smoking, a factor which
could influence the gut microbiota composition. Future
studies were needed to analyze how the smoking
influenced the gut microbiota composition in the
different phases of life cycle of subjects. Finally, we
found that the medication status of subjects could not
significantly affect our results. However, limited by the
relatively small samples, this conclusion was needed
future studies to further validate.
In conclusion, in this study, we found that there were
age-specific differential changes on gut microbiota
composition in patients with MDD, and identified some
age-specific differentially abundant bacterial taxa in
MDD patients. Our findings would provide a novel
perspective to uncover the pathogenesis underlying
MDD, and potential gut-mediated therapies for MDD
patients. Limited by the small number of subjects, the
results of the present study were needed future studies
to validate and support.
MATERIALS AND METHODS
Subject recruitment
This study was approved by the Ethical Committee of
Chongqing Medical University and conformed to the
provisions of the Declaration of Helsinki. In total, there
were 27 young HCs (aged 18-29 years) and 25 young
MDD outpatients (aged 18-29 years) in the young
group; there were 44 middle-aged HCs (aged 30-59
years) and 45 middle-aged MDD outpatients (aged 30-
59 years) in the middle-aged group. Most of MDD
patients were first-episode drug-naïve depressed
subjects. There were only seven young MDD patients
and 14 middle-aged MDD patients receiving
medications. The detailed information of these included
subjects was described in Table 1. All HCs were
recruited from the Medical Examination Center of
Chongqing Medical University, and all MDD patients
were recruited from the psychiatric center of Chongqing
Medical University. MDD patients were screened in the
baseline interview by two experienced psychiatrists
using the DSM-IV (Diagnostic and Statistical Manual
of Mental Disorders, 4th Edition)-based Composite
International Diagnostic Interview (CIDI, version2.1).
The Hamilton Depression Rating Scale (HDRS) was
used to assess the depressive symptoms of each patient,
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Table 1. Demographic and clinical characteristics of MDD patients and HCsa.
Young group (18-29 years) Middle-aged group (30-59 years)
HC MDD p-value HC MDD p-value
Sample Size 27 25 – 44 45 –
Age (years)c 24.96±2.31 24.0±3.74 0.26 47.16±8.07 44.96±7.76 0.19
Sex (female/male) 19/8 18/7 0.89 34/10 31/14 0.37
BMI 21.53±2.37 22.13±2.24 0.35 23.23±2.33 22.64±2.64 0.26
Medication (Y/N) 0/27 7/18 – 0/44 14/31 –
HDRS scores 0.29±0.61 22.64±3.18 <0.00001 0.34±0.74 23.0±4.61 <0.00001
aAbbreviations: HDRS: Hamilton Depression Rating Scale; HCs: healthy controls; MDD: major depressive disorder; BMI: body mass index.
and those patients with HDRS score >=17 were
included. Meanwhile, MDD patients were excluded if
they had other mental disorders, illicit drug use or
substance abuse, and were pregnant or menstrual
women. HCs were excluded if they were with mental
disorders, illicit drug use or systemic medical illness.
All the included subjects provided written informed
consent before sample collection.
16s rRNA gene sequencing
We used the standard PowerSoil kit protocol to extract
the bacterial genomic DNA from the fecal samples.
Briefly, we thawed the frozen fecal samples on ice and
pulverized the samples with a pestle and mortar in
liquid nitrogen. After adding MoBio lysis buffer into
the samples and mixing them, the suspensions were
centrifuged. The obtained supernatant was moved into
the MoBio Garnet bead tubes containing MoBio buffer.
Subsequently, we used the Roche 454 sequencing (454
Life Sciences Roche, Branford, PA, USA) to extract the
bacterial genomic DNA. The extracted V3-V5 regions
of 16S rRNA gene were polymerase chain reaction-
amplified with bar-coded universal primers containing
linker sequences for pyrosequencing [29].
The Mothur 1.31.2 (http://www.mothur.org/) was used
to quality-filtered the obtained raw sequences to
identify unique reads [30]. Raw sequences met any one
of the following criteria were excluded: i) less than
200bp or greater than 1000bp; ii) contained any
ambiguous bases, primer mismatches, or barcode
mismatches; and iii) homopolymer runs exceeding six
bases. The remaining sequences were assigned to
operational taxonomic units (OTUs) with 97%
threshold, and then taxonomically classified according
to Ribosomal Database Project (RDP) reference
database [31]. We used these taxonomies to construct
the summaries of the taxonomic distributions of OTUs,
and then calculated the relative abundances of gut
microbiota at different levels. The abovementioned
procedure and most of data were from our previous
studies [15, 16].
Statistical analysis
Richness was one of the two most commonly used alpha
diversity measurements. Here, we used two different
parameters (Chao1 and ACE) to estimate the OTU
richness [32, 33]. The orthogonal partial least squares
discriminant analysis (OPLS-DA) was a multivariate
method, which was used to remove extraneous variance
(unrelated to the group) from the sequencing datasets. The
LEfSe was a new analytical method for discovering the
metagenomic biomarker by class comparison. The
bacterial taxa with LDA score>2.0 were viewed as the
differentially abundant bacterial taxa responsible for the
differences between different groups. Here, both OPLS-
DA [34, 35] and LEfSe were used to reduce the
dimensionality of datasets and identify the differentially
abundant bacterial taxa (the Family level and Genus level)
that could be used to characterize the significant
differences between HCs and MDD patients. Meanwhile,
we used the Random Forest algorithm to identify the
critical discriminatory OTUs. The ROC curve analysis
was used to assess the diagnostic performance of these
identified differential bacterial taxa. The AUC was the
evaluation index. Finally, we used the LEfSe to reveal the
changes of microbial abundances at Family level and
Genus level in HCs and MDD patients, respectively.
ACKNOWLEDGMENTS
Our sincere gratitude is extended to Professors Delan
Yang and Hua Hu from Psychiatric Center of the First
Affiliated Hospital of Chongqing Medical University
for their efforts in sample collection.
CONFLICTS OF INTEREST
The authors declare no financial or other conflicts of
interest.
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FUNDING
This work was supported by the National Key R&D
Program of China (2017YFA0505700), the Non-profit
Central Research Institute Fund of Chinese Academy of
Medical Sciences (2019PT320002300), the Natural
Science Foundation Project of China (81820108015,
81701360, 81601208, 81601207), the Chongqing
Science and Technology Commission
(cstc2017jcyjAX0377), the Chongqing Yuzhong
District Science and Technology Commission
(20190115), and supported by the fund from the Joint
International Research Laboratory of Reproduction &
Development, Institute of Life Sciences, Chongqing
Medical University, Chongqing, China, and also
supported by the Scientific Research and Innovation
Experiment Project of Chongqing Medical University
(CXSY201862, CXSY201863).
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