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Article
Effect of Gestational Diabetes on Postpartum Depression-like
Behavior in Rats and Its Mechanism
Runlong Zhao 1,2 , Yalin Zhou 1,2 , Hanxu Shi 1,2 , Wanyun Ye 1,2 , Ying Lyu 1,2 , Zhang Wen 1,2 , Rui Li 1,2
and Yajun Xu 1,2,3, *
1
2
3
*
Citation: Zhao, R.; Zhou, Y.; Shi, H.;
Ye, W.; Lyu, Y.; Wen, Z.; Li, R.; Xu, Y.
Effect of Gestational Diabetes on
Postpartum Depression-like Behavior
in Rats and Its Mechanism. Nutrients
2022, 14, 1229. https://doi.org/
10.3390/nu14061229
Academic Editor: Dan Ramdath
Received: 9 February 2022
Accepted: 11 March 2022
Published: 14 March 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
Department of Nutrition and Food Hygiene, School of Public Health, Peking University,
No. 38 Xueyuan Road, Beijing 100083, China; 1510306204@pku.edu.cn (R.Z.); zylyingyang@163.com (Y.Z.);
shihanxu@pku.edu.cn (H.S.); yewanyun_vera@bjmu.edu.cn (W.Y.); lybjmu@126.com (Y.L.);
1710306240@pku.edu.cn (Z.W.); lr15321657608@163.com (R.L.)
PKUHSC—China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development,
No. 38 Xueyuan Road, Beijing 100083, China
Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Peking University,
No. 38 Xueyuan Road, Beijing 100083, China
Correspondence: xuyajun@bjmu.edu.cn; Tel.: +86-010-82802552
Abstract: Recent studies have reported a strong association between gestational diabetes mellitus
(GDM) and postpartum depression (PPD), but little is known about the underlying physiological
mechanism. In this study, a GDM rat model was used to evaluate the direct effect of GDM on PPD
and to explore the mechanism. After parturition, the GDM dams were divided into two groups:
blood glucose not recovered group (GH group) and blood glucose recovered group (GL group).
Fasting plasma glucose (FPG), cortisol (COR) and serotonin (5-hydroxytryptamine, 5-HT) metabolism
were continuously monitored during the lactation period, until postnatal day 21. PPD was evaluated
by behavioral tests. At the endpoint, the expression of the key enzymes of Trp metabolic pathway
in colon and brain tissues was analyzed by immunohistochemistry and western blot. The microbe
composition of colonic contents was determined by 16S rDNA gene sequencing. The results showed
that GDM induced postpartum depression-like behavior in rats. The HPA axis hormone did not show
the typical stress state of depression, but the level of 5-HT decreased significantly in serum, prefrontal
cortex and hippocampus, and the Kyn/Trp ratio increased significantly in serum and prefrontal
cortex, implying the switch of the tryptophan (Trp) metabolism from the 5-HT pathway to the
kynurenine (Kyn) pathway. The expression of Indoleamine 2,3-dioxygenase (IDO), a key rate-limiting
enzyme in Kyn metabolism, was up-regulated in the colon and brain, which was an important reason
for this switch. This switch was accelerated by a decrease in the expression of tryptophan hydroxylase
(TPH), a key enzyme of the 5-HT production pathway, in the colon. GDM dams displayed significant
changes in gut microbiome profiles, which were correlated with depression. The ratio of Firmicutes
to Bacteroidetes decreased. Lactobacillus and Bacteroides were negatively correlated with 5-HT level
and positively correlated with Kyn level, whereas Clostridium XlVa and Ruminococcus were positively
correlated with 5-HT level. These results suggest that GDM disrupts both the Trp pathway and the
composition of the gut microbiota, which provide a putative physiological basis for PPD.
published maps and institutional affiliations.
Keywords: gestational diabetes mellitus; postpartum depression; tryptophan metabolism; serotonin;
kynurenine; gut microbiota
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Postpartum depression (PPD) is a common type of puerperal mental disorder characterized by persistent and severe depression and a range of symptoms. Symptoms usually
appear within 2 weeks after birth. It has been reported that one in seven new mothers is
affected by PPD and that the prevalence of depression within one year after delivery is
21.9% [1]. PPD directly affects the mother’s mental health and social adaptability and also
Nutrients 2022, 14, 1229. https://doi.org/10.3390/nu14061229
https://www.mdpi.com/journal/nutrients
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interferes with her ability to adequately breastfeed and care for her child [2]. Under this
circumstance, the infants are at risk of suffering from delayed, even impaired, physical and
mental development. Some women with postpartum major depression may experience
suicidal ideation or obsessive thoughts of harming their infants [3].The mechanism of PPD
has not been fully elucidated; however, it is generally accepted that both physiological and
psychosocial factors play an important role.
One of the known PPD mechanisms is related to the hypothalamic-pituitary-adrenal
(HPA) axis signaling pathway. Researchers have demonstrated in mice that postpartum
HPA axis dysfunction is sufficient to induce postpartum depression, with maternal increased HPA axis excitability and glucocorticoid secretion [4–6]. Changes in neurotransmitters, particularly serotonin (5-hydroxytryptamine, 5-HT), which induces happy emotions,
are the pathophysiology basis of mood disorders [7]. 5-HT is mainly metabolized by tryptophan (Trp) and distributed in the intestinal mucosa and brain, with some circulation in
peripheral blood. When tryptophan metabolism is disordered, 5-HT synthesis is decreased,
and kynurenine (Kyn) synthesis is increased (Figure 1). However, Kyn and its downstream
metabolites have been shown to be associated with depression and neuronal damage [7,8].
Figure 1. Trp metabolic pathway.
In recent years, there has been a growing body of research on the effects of gut microbes
on mood through the gut–brain axis; high levels of Faecalibacterium and Coprococcus were
associated with good mental health, whereas high levels of flavobacterium were associated
with poor mental health. The composition of the gut microbiota of patients with depression
is different from that of normal people, which may be related to the ability of microorganism
to produce neuroactive substances [9,10].
A large number of studies have confirmed that patients with gestational diabetes
mellitus (GDM) have a higher PPD risk than women with normal blood glucose levels
during pregnancy [11–13]. Although several studies have focused on the relationship
between GDM and PPD, most have found it difficult to determine the effect of plasma
glucose on the development of PPD in patients with GDM; since people are in a complex
social environment, it is difficult to completely remove the social and environmental factors
and independently study the effect of the physiological factors of GDM on PPD. As a result,
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most studies attribute the association between GDM and PPD solely to psychosocial effects,
that is, the social and psychological stress caused by the disease [14,15]. Whether GDM
itself has direct effect on the occurrence of PPD and the mechanism are seldom reported.
In this study, the direct effect of GDM on the development of PPD was studied with a
GDM rat model, excluding confounding by social and psychological factors, in order to
provide a scientific basis for PPD early prevention and intervention.
2. Materials and Methods
2.1. Animals
Sprague-Dawley (SD) rats of specific-pathogen-free (SPF) degree were used throughout the study. Female and male rats were obtained from the Department of Laboratory
Animal Science of Peking University (Beijing, China, SCXK-2012-0015) and kept in a barrier environment with a temperature of 22 ± 2 ◦ C, humidity of 50–60%, and 12 h/12 h
light/dark cycle. Rats were provided distilled water ad libitum. The experiment was
approved by the ethics committee of Peking University (No. LA2021107).
2.2. Establishing the GDM Model
The combination of high-fat diet (HFD) with an intraperitoneal injection of 30 mg/kg
streptozotocin (STZ) was adopted to establish the GDM rat model [16]. Four-week-old
female rats were randomly grouped into the control group (CON) and GDM group after
adaptive feeding for one week. The rats in the CON group were fed with basic chow,
and the rats in the GDM group were fed HFD (HFD formula: lard 10%, sucrose 15%, egg
yolk powder 15%, casein 5%, cholesterol 1.2%, sodium cholate 0.2%, calcium bicarbonate
0.6%, stone powder 0.4%, rat maintenance feed 52.6%) for 8 weeks. After that, following
fasting for 12 h, tail vein blood was collected, and the fasting plasma glucose (FPG) was
measured using a rapid blood glucose detector (OneTouch Ultraeasy, LifeScan Inc., Milpitas,
CA, USA). Rats with FPG > 6.67 mmol/L were removed from the study due to violating
the definition of GDM (carbohydrate intolerance of variable severity with onset or first
recognition during pregnancy, not before gestation). Eligible female rats were mated with
healthy male rats overnight at 1:1. The next morning, vaginal smears were observed
under a microscope to judge the successful pregnancy. The presence of sperm in the
vaginal smear was considered gestation day 0 (GD0). On GD5, after fasting overnight,
pregnant rats in the GDM group were intraperitoneally injected with 30 mg/kg 1% STZ
(STZ dissolved in 0.1 mol/L sodium citrate solution, pH = 4.2), and the rats in CON group
were intraperitoneally injected with 0.1 mol/L sodium citrate solution. Seventy-two hours
after injection, pregnant rats with FPG > 6.67 mmol/L were considered a successful model
of GDM [17].
2.3. Experimental Groups
All the dams were allowed to give birth naturally and breastfeed their offspring.
According to the FPG levels measured on day 4 postpartum, the dams of the GDM group
were further divided into two groups, the postpartum blood glucose not recovered group
(GH group) and recovered group (GL group). Those dams with FPG levels exceeding two
standard deviations of the mean FPG of the CON group were classified as the GH group,
and the remain dams in the original GDM group were classified as the GL group.
2.4. Behavioral Testing
After delivery, all rats were fed with basic diet, and a series of behavioral tests were
carried out.
2.4.1. Open Field Test
The open field test (OFT) was used to assess the exploratory and anxious behavior of
rats in a new environment [18]. The open box was made of opaque black organic plastic
(100 × 100 × 50 cm) and divided into 25 identical squares (20 × 20 cm) with white stripes.
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The square area in the middle of the box was the central region (60 × 60 cm), which included
9 squares. This test was performed in a quiet and dark (visibility was 5 m) room. A single
rat was placed in the center of the area and was let for exploration for 3 min. After each
test, the box was swabbed with 75% alcohol. The number of squares crossed by each rat
(with three paws crossing the line), the number of standing times (with two front paws
off the ground) and the time spent in the central region (with all four paws in the central
region) were recorded. The OFT was carried out 3 times, which were on PND4, PND11
and PND18.
2.4.2. Elevated Plus Maze Test
The elevated plus maze (EPM) test is one of the most widely used models for evaluating rat depression-like behavior, taking advantage of animals’ exploratory nature to novel
environments and their fear of overhanging arms to form a conflict to examine animal
depression [19]. The EPM instrument consisted of two open arms (50 × 10 cm) and two
closed arms (50 cm × 10 cm × 40 cm) that originated from a central platform (10 × 10 cm).
The maze was 50 cm above the ground. This test was performed in a quiet and dark
(visibility was 5 m) room. Each rat was placed on the central platform, with their head
facing toward one of the open arms. The open arm entry times, close arm entry times and
open arm duration were recorded within 5 min after placement, and the percentage of time
entering the open arm and the percentage of time staying in the open arm were calculated.
The rat was considered to enter a new arm when it introduced four paws in the arm. The
EPM test was performed on PND6.
2.4.3. Forced Swimming Test
The forced swimming test (FST) was carried out on PND8 to evaluate the depressive-like
behavior of each rat [20]. In the FST, each rat was placed in a water tank (60 × 40 × 50 cm),
where the water depth was 30 cm and the water temperature was 24 ± 1 ◦ C. The rat was
allowed to adapt to the water for 2 min, and after that, the duration of immobility time of
each rat was recorded during the next 4 min. The rats were not allowed to climb the upper
edge of the water tank or to stand on the bottom of the tank.
2.4.4. Sucrose Preference Test
The sucrose preference test (SPT), which is a method for detecting anhedonia in
animals based on their preference for sweet taste, was performed on PND21. Anhedonia
refers to a decline in the ability to experience pleasure, which is an important feature of
depression [21]. Each rat was first reared in a single cage without water for 24 h and then
given one bottle of 1% sucrose water and one bottle of purified water at the same time. The
consumption of sucrose water and pure water during the next 1 h was recorded for each
rat. The sucrose preference (%), which is calculated as sucrose water consumption divided
by the sum of sucrose water and pure water consumption, was calculated.
The schedule of the behavioral tests in the present study is shown in Figure 2.
Figure 2. Schedule of behavioral tests. OFT, open field test; EPM, elevated plus maze test; FST, forced
swim test; SPT, sucrose preference test.
2.5. Sample Collection
The body weight and blood glucose were monitored regularly during lactation, and
abnormal conditions such as rejection of lactation, lethargy, immobility and vaginal bleed-
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ing were observed and recorded. A blood sample was collected from the orbital venous
plexus of each rat on PND7 and PND14. After the behavior test on PND21, each rat was
fasted for 12 h and then sacrificed. Blood samples were collected from the orbital venous
plexus and serum were separated by 1000× g for 15 min. The serum was stored at −80 ◦ C
for further analysis. The hippocampus and prefrontal cortex (prefrontal cortex) of the brain
were collected and excised on ice. The samples of brain tissue were stored at −80 ◦ C in
sterile enzyme-free cryotubes. The right prefrontal cortex and hippocampus of each animal
were fixed with 4% paraformaldehyde for immunohistochemical staining. The distal 5 cm
colon was dissected rapidly, and the colon contents were collected into a sterile EP tube
in a sterile environment. The colon and samples of contents were snap-frozen with liquid
nitrogen and stored at −80 ◦ C for future use.
2.6. ELISA Quantification of Neurotransmitter
Enzyme-linked immunosorbent assay (ELISA) was used to detect hormone and tryptophan related products in serum, the hippocampus and the prefrontal cortex. The hippocampus and prefrontal cortex were exposed to 9 times the volume of PBS (pH = 7.4), the
homogenate was 5000× g, at 4 ◦ C for 5 min. The supernatant was collected for ELISA assay
according to the instructions of the Kit (Nanjing Jiancheng Bioengineering Institute Co.,
Nanjing, Zhejiang, China).
2.7. Immunohistochemistry (IHC)
Fixed tissue was cut into chips and rehydrated in a graded series of ethanol. The tissue
chip was placed in the citrate buffer (pH = 6.0) at 28 ◦ C for 40 min to repair the antigen, then
incubated with 3% H2 O2 at 28 ◦ C for 25 min to inactivate the endogenous peroxidase and
washed with PBS (pH = 7.4) 3 times. Then, 3% BSA was added, and the chip was sealed at
28 ◦ C for 30 min. After gently shaking off the solution, chips were incubated overnight at
4 ◦ C with the antibody of Anti-IDO2 (ab288067, Abcam, Shanghai, China) or Anti-TPH2
(A06002-2, BOSTER, Wuhan, Hubei, China). On the next day, the biotin-labeled secondary
antibody and horseradish peroxidase (HRP)-conjugated streptavidin were added to the
tissue chip and incubated at 28 ◦ C for 50 min. Finally, fresh DAB color-developing solution
was added. The chips were scanned by a panoramic scanner PANNORAMIC (3DHISTECH,
Budapest, Hungary). The software Aipathwell (Servicebio, Wuhan, Hubei, China) was
used to analyze the staining intensity and rate of the positive cells. The positive grade was
evaluated first: negative, without coloring, 0 point; weak positive, light yellow, 1 point;
medium positive, brownish yellow, 2 points; strong positive, sepia, 3 points. Then, we
analyzed and calculated the grade, the measurement area, the positive area, the tissue area
in the measurement area, the cumulative optical density (IOD), the mean optical density
(MOD) and the positive area density (AD). The histochemistry score (H-score) was used
for each slide in order to evaluate the staining intensity. H-score = ∑ (pi × i) = (percentage
of weak intensity × 1) + (percentage of moderate intensity × 2) + (percentage of strong
intensity × 3) [22].
2.8. Western Blot
The proteins of the prefrontal cortex, hippocampus and colon were extracted by RIPA
lysis buffer (Sigma, St Louis, MO, USA) and homogenized on ice using a glass grinder.
Following 20,000× g, 4 ◦ C for 20 min, the protein in the supernatant of concentration was
determined by BCA protein assay reagent (Thermo Scientific Rockford, IL, USA). After
boiling with the loading buffer, it was separated by electrophoresis and transferred to the
polyvinylidene difluoride (PVDF) membranes. The membranes were sealed with 5% nonfat milk in TBS-T solution and placed at room temperature for 4 h. The membranes were
incubated overnight at 4 ◦ C with the following primary antibodies: (1) rabbit anti-TPH1
antibody (1:1000, Boster, Wuhan, Hubei, China); (2) rabbit anti-IDO2 antibody (1:2000,
Boster, Wuhan, Hubei, China); (3) rabbit anti-TPH2 antibody (1:1000, Boster, Wuhan, Hubei,
China). After incubation, the membranes were washed with TBS-T solution 3 times. The
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membranes were incubated with HRP-labeled secondary antibody (Goat Anti-Rabbit IgG,
1:10,000, ABCAM, Shanghai, China) at room temperature for 4 h and then washed with
TBS-T solution 3 times. The bands were visualized with ECL (Millipore Corporation,
Billerica, MA, USA). All experiments were repeated at least three times. Image-Pro Plus
software (Media Cybernetics, Inc. Rockville, MD, USA) was used to analyze the gray
intensity values of bands and normalize the results to the counterparts of β-actin.
2.9. Microbiota Analysis by 16S Sequencing
2.9.1. DNA Extraction and PCR Amplification
Microbial DNA was extracted using the QIAamp® FastDNA StoolMini Kit (QIAGEN,
Hilden, North Rhine-Westphalia, Germany) according to the manufacturer’s protocol. The
V3-V4 region of the bacteria 16S rDNA genes were amplified by PCR (95 ◦ C for 3 min,
followed by 30 cycles at 98 ◦ C for 20 s, 58 ◦ C for 15 s, and 72 ◦ C for 20 s and a final extension
at 72 ◦ C for 5 min) using barcoded primers 341F (50 -CCTACGGGRSGCAGCAG-30 ) and
806R (50 -GGACTACVVGGGTATCTAATC-30 ). PCR reactions were performed in a 30 µL
mixture containing 15 µL of 2 × KAPA Library Amplification ReadyMix, 1 µL of each
primer (10 µM), 50 ng of template DNA and ddH2 O.
2.9.2. Illumina Sequencing and Data Processing
PCR products were detected by 2% agarose gels and purified by AxyPrep DNA Gel
Extraction Kit (Axygen Biosciences, Union City, CA, USA). After that, Thermo NanoDrop
2000 (Thermo Fisher Scientific, Waltham, MA, USA) and 2% agarose gel electrophoresis
were used for library quality control. A specific 16S primer was designed to amplify the
specific region, and about 425 bp amplified fragment was obtained. The paired-end data of
PE250 were obtained by sequencing using the Illumina platform, and a longer sequence
was obtained by splicing, which was used for 16S analysis. OTUs (Operational taxonomical Units) were clustered with 97% similarity cutoff using Usearch (version 7.0.1090
http://drive5.com/usearch/, accessed on 15 October 2021); each out represents one species.
In order to avoid the analysis bias caused by the different sample sequence data size, if the
sequence depth was enough, according to the minimum sequence number matching to the
OTUs, random drawing was carried out, and Alpha diversity was analyzed. A representative sequence of the read was extracted from each OTU, which was compared with RDP
Classifier (http://rdp.cme.msu.edu/, accessed on 15 October 2021); the species of each
OTU was classified, and the species abundance table was obtained for follow-up analysis.
2.10. Statistical Analysis
All data were analyzed using SPSS (version 25.0, Armonk, NY, USA). The results were
all continuous variables and were expressed by mean ± SEM. When normality and equal
variance between sample groups were achieved, one-way ANOVA followed by Fisher’s
Least Significant Difference (LSD) was used to compare the differences among groups. If
failed, one-way ANOVA followed by Tamhane’s test was performed. p < 0.05 was the
threshold for statistical significance.
3. Results
3.1. Fasting Plasma Glucose Monitor
On PND4, GDM rats with fasting plasma glucose higher than 6.0 mmol/L were put
into the GH group, and the other GDM rats were assigned to the GL group. During
lactation, the FPG (Figure 3b) of the GH group was significantly higher than that of the
CON and GL group. After grouping, the FPG in the gestational period was higher than
the CON in the GH group and GL group, and the FPG of the GH group also higher than
the GL group. After 8 weeks of HFD, the body weight (Figure 3a) of the CON group was
307.6 ± 4.9 g (n = 10), that of the GH group was 327.8 ± 5.7 g (n = 12), that of the GL group
was 319.0 ± 7.9 g (n = 12) and that of the GH group and GL group was increased, but the
difference was not statistically significant (p = 0.151).
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Figure 3. The body weight and FPG during lactation period. (a) The maternal body weight during
lactation. (b) The fasting plasma glucose during pregnancy and lactation. * significant differences vs.
CON group, p < 0.05. ** significant differences vs. CON group, p < 0.01. # significant differences vs.
GH group, p < 0.05. ## significant differences vs. GH group, p < 0.01.
3.2. GDM-Induced Postpartum Depression-like Behavior in Rats
After parturition, three dams in the GH group were found to refuse to feed their
offspring; however this was not the case in the CON and GL groups. In the first OFT at
PND4 (Figure 4a), the activity score of the GH group was significantly lower than that in
the CON group. No significant difference was found between the GL and CON groups;
however, the absolute value of the average score of the GL group was lower than that of
CON. The residence time in the central region (Figure 4b) of the GH and GL groups was
significantly lower than that of the CON group.
Figure 4. Results of postnatal behavior test in CON, GH and GL group. (a) Activity point in the OFT.
(b) Central region times (s) in the OFT. (c) Open arm entry (%) in the EPM in PND6. (d) Open arm
duration (%) in the EPM test on PND6. (e) Immobility times (s) in the FST on PND8. (f) Sucrose
consumption ratio (%) in the SPT on PND21. * significant differences vs. CON group, p < 0.05.
** significant differences vs. CON group, p < 0.01.
In the EPM test, comparing with the CON group, the time percentage of staying in
the open arm (Figure 4c) was significantly lower in the GH and GL groups. The time of
entering the open arm (Figure 4d) in the GH and GL groups was significantly lower than
that in CON. It was shown in the FST that that the immobility time of the GH group was
significantly lengthened compared with CON; however, no significant difference was found
between the GL and CON group (Figure 4e). The SPT showed no significant difference
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between the groups, although from the bar chart, the sucrose preference of the GH group
was slightly lower than the CON group (Figure 4f).
Based on the above behavioral results, GDM dams were more likely to develop
postpartum depression, and those whose blood sugar did not recover after delivery were
at greater risk.
3.3. HPA Axis Hormone
As shown in Figure 5, the average level of serum COR in GH group dams was
significantly lower than that of the CON group. Interestingly, the level of COR in the GL
group was almost the same as that in CON during lactation, but decreased significantly
after weaning (p = 0.002). Accordingly, the level of adreno-cortico-tropic-hormone (ACTH)
was significantly increased, and corticotropin releasing hormone (CRH) was significantly
decreased (p = 0.037). The changes of ACTH and CRH in the GH group were greater than
in the GL group.
Figure 5. The levels of serum HPA axis hormones. (a) The serum COR level during lactation. (b) The
serum CRH level on PND22. (c) The serum ACTH level on PND22. * significant differences vs. CON
group, p < 0.05. ** significant differences vs. CON group, p < 0.01.
3.4. Expression of Trp Pathway Neurotransmitters
During the lactation period, the trends of serum Trp and its major metabolites were
not significantly different among the groups (Figure 6). The 5-HT level in the GH group
was significantly lower than that in CON group; however, no such significance was found
between the GL and CON groups. The 5-HT/Trp ratio decreased significantly in the GH
group. The content of Kyn, another major metabolite of Trp, was slightly higher in the GH
and GL groups than in the CON group, although no statistical difference was found.
Trp and its metabolites in serum, the hippocampus and the prefrontal cortex were
measured on PND22. In general, 5-HT concentration and 5-HT/Trp ratio in the GH and
GL groups were lower than those in the CON group, and the ratios of Kyn, Kyn/Trp and
5-HIAA/5-HT were higher. In the serum (Figure 6a), the concentration of 5-HT and the
ratio of 5-HT/Trp in the GH and GL groups were significantly lower than those in the
CON group, and the ratio of 5-HIAA/5-HT was significantly higher than that in the CON
group. The Kyn and Trp ratios in the GH group were significantly higher than those in the
CON group. In the hippocampus (Figure 6b), the 5-HT and 5-HT/Trp in the GH group was
significantly lower than that in CON, the ratio of 5-HIAA/5-HT was significantly higher
than that in the CON group, and the ratio of 5-HT/Trp in the GL group was significantly
lower than that in the CON group, the differences were statistically significant. In the
prefrontal cortex (Figure 6c), the ratio of Kyn/Trp in the GH group was significantly higher
than in the CON group. The increase in Kyn/Trp indicated the trend that Trp metabolism
was changed from the Trp-5-HT pathway to the Trp-Kyn pathway. Compared with the GH
group, the level of Kyn and the ratio of Kyn/Trp in the GL group increased to some extent,
and the ratio of 5-HT/Trp also increased, which indicated that the Trp-5-HT metabolic
pathway was improved.
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Figure 6. Trp and its metabolites levels in (a) serum, (b) the hippocampus and (c) the prefrontal
cortex. * significant differences vs. CON group, p < 0.05. ** significant differences vs. CON group,
p < 0.01.
3.5. Expression of Key Enzymes in Trp Metabolic Pathway in the Brain and Colon
The expression of IDO in the prefrontal cortex and hippocampus of the GH group was
significantly increased, while the expression of TPH2 was decreased. The H-scores of the
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prefrontal cortex and hippocampus chips of the GH group were significantly increased
compared to CON (Table 1, Figure 7). There was no significant difference in the intensity
of positive staining among groups, but the positive area of the GH group was the largest.
Similarly, a trend was found in TPH2 expression both in the prefrontal cortex and in
the hippocampus of the GH group; however, no statistical difference was found in the
hippocampus (Table 1, Figure 8). The IDO expression of the GL group was slightly higher
than that of CON in the prefrontal cortex (p = 0.061) and parallel the level of the CON group
in the hippocampus.
Table 1. Comparison of IDO and TPH2 immuno-positive MOD, AD and H-score in different brain
regions of rats in each group.
Tissue-Protein
IHC
CON
GH Group
GL Group
prefrontal cortex-IDO
MOD
AD
H-score
0.161 ± 0.001
0.017 ± 0.002
10.737 ± 1.024
0.158 ± 0.001
0.038 ± 0.004 **
24.495 ± 2.834 **
0.159 ± 0.001
0.027 ± 0.004 #
17.136 ± 2.690 #
hippocampus-IDO
MOD
AD
H-score
0.156 ± 0.002
0.020 ± 0.004
12.569 ± 2.104
0.160 ± 0.001
0.028 ± 0.003 *
17.725 ± 1.991 *
0.155 ± 0.001
0.019 ± 0.002 #
12.247 ± 1.176 #
prefrontal cortex-TPH2
MOD
AD
H-score
0.233 ± 0.003
0.005 ± 0.000
3.089 ± 0.161
0.235 ± 0.006
0.004 ± 0.000 **
2.342 ± 0.114 **
0.237 ± 0.005
0.004 ± 0.000
2.644 ± 0.237
hippocampus-TPH2
MOD
AD
H-score
0.259 ± 0.002
0.006 ± 0.001
3.990 ± 0.413
0.262 ± 0.004
0.005 ± 0.001
3.407 ± 0.418
0.263 ± 0.003
0.006 ± 0.000
3.741 ± 0.433
* p < 0.05 and ** p < 0.01 indicate significant differences vs. CON group. # significant differences vs. GH group,
p < 0.05.
Figure 7. Expression of IDO in prefrontal cortex (a–c) and hippocampus (d–f) of three groups of rats.
(a,d) CON group, (b,e) GH group, (c,f) GL group. DAB × 400.
Western blotting of the colon showed that the expression of TPH1 was significantly
decreased (p = 0.031) and IDO was increased in the GH group (p = 0.066) (Figure 9).
The expression of IDO in the GL group was significantly increased (p = 0.004), while the
expression of TPH1 was decreased (p = 0.064). The above results revealed that GDM
affected the expression of key enzymes in the Trp pathway in the colon, prefrontal cortex
and hippocampus, TPH changed significantly in the colon and prefrontal cortex, and
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IDO changed significantly in the colon, prefrontal cortex and hippocampus. Additionally,
compared with the GH group, the GL group was improved.
Figure 8. Expression of TPH2 in prefrontal cortex (a–c) and hippocampus (d–f) of three groups of
rats. (a,d) CON group, (b,e) GH group, (c,f) GL group. DAB × 400.
Figure 9. Western blot of rate-limiting enzymes that induced Trp metabolism in colon. * significant
differences vs. CON group, p < 0.05.
3.6. Gut Microbiota Changes
The Chao1 index and Shannon index (Figure 10a) of the GH group decreased significantly, which indicated that the diversity of gut microbiota in the GH group decreased.
However, no such changes found in the GL group. Through the species cluster analysis
(Figure 10c), the composition of the CON and GL groups is roughly divided into one cluster
and the GH group itself is another cluster, indicating that the GH group and the other two
groups had significant differences in the composition of gut microbiota.
Taxonomic shifts were also investigated, and the ratio of gut microbiota varied among
the different groups at the phylum level (Figure 10d). The most common were Firmicutes,
Bacteroidetes and Proteobacteria. The ratio of Firmicutes/Bacteroidetes (Figure 10b) in the GH
and GL groups increased, and the changes in the GH group were statistically significant
(p = 0.015). In addition, the relative abundance of Actinobacteria decreased in the GH
group and increased in the GL group (p = 0.025) compared with CON. GDM increased
the Firmicutes/Bacteroidales ratio, due to an underlying change in the abundance of the
orders Clostridiales and Bacteroidales. The decrease of Clostridiales in the GH and GL groups
(p = 0.033) and the increase of Bacteroidales in the GH group (p = 0.021) were both statistically
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significant. The family-level results (Figure 10d) showed that the relative abundance of Prevotellaceae in the GH group was significantly higher than that of the CON group (p = 0.018).
The relative abundance of Ruminococccaceae, Peptococciaceae 1 and Coriobacteriaceae in the
GL group was significantly higher than that of the CON group. In addition, the highest
relative abundance, Lachnospirnaceae, was slightly decreased in the GH and GL groups. At
the genus level (Figure 11), the relative abundance of typical microorganisms, Prevotella,
Lactobacillus and Paraprevotella, increased in the GH group, with Helicobacter, Clostridium
XlVa and Ruminococcus decreased.
Figure 10. Species diversity and composition of gut microbiota. (a) Chao1 and Shannon index
describing microbial α diversity. (b) Firmicutes:Bacteroidetes ratio and the relative abundance of
Actinobacteria. (c) Cluster analysis of species at genus level. (d) Bar chart of relative abundance of
species at phylum and family levels. * significant differences vs. CON group, p < 0.05. # significant
differences vs. GH group, p < 0.05. ## significant differences vs. GH group, p < 0.01.
In order to further elucidate the potential relationship between Trp metabolic and
gut microbiota, the correlation between serum markers, behavioral results and microbial
relative abundance was analyzed. There was a significant positive correlation between
Ruminococcaceae and 5-HT in serum, and a significant negative correlation between Lactobacillus, Bacterioides and 5-HT. In addition, there was a strong positive correlation between
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Bacteroides and Kyn level, although it was not statistically significant. Clostridium XlVa
showed a significant positive correlation with behavioral outcomes and improved15FST
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and SPT performance. Prevotella and Lactobacillus showed a negative relationship with the
performance in behavior tests.
a
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