Prenatal stress increases corticosterone levels in offspring by impairing placental glucocorticoid barrier function

doi:10.21203/rs.3.rs-3303973/v1

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Prenatal stress increases corticosterone levels in offspring by impairing placental glucocorticoid barrier function

https://doi.org/10.21203/rs.3.rs-3303973/v1

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Background: Exposure to high cortisol levels in fetus of prenatal stress (PS) has adverse effects on growth, which is related to placental glucocorticoid (GC) barrier. DNA methylation is a potential prenatal planning mechanism in embryonic stage, however, the epigenetic regulation of placental GC barrier related genes on the PS resulting higher GC is unclear. This study was to test the hypothesis that PS would elevate cortisol which was associated with GC-related placenta genes (11β-HSD2, P-gp, NR3C1, and FKBP5) based on the DNA methylation.

Method: PS model was established by chronic unpredictable mild stress (CUMS). DNA methylation in GC-related genes was analyzed using reduced representation bisulfite sequencing (RRBS) and confirmed results using MethylTarget™ sequencing. The genes expression were subjected to qRT-PCR and Western blot.

Results: Offspring of PS had increased plasma corticosterone levels. GC-related gene (P-gp(abcb1a) and FKBP5) were identified by RRBS. We further verified DNA methylation and gene expression, including 43 differentially methylated sites and 2 differentially methylated regions. We found P-gp was hypermethylation and low expression, FKBP5 was hypermethylation low translational and high transcriptional levels. The expressions of 11β-HSD2 decreased, the NR3C1 mRNA expression was inhibited, and the protein expression increased.

Discussion:This study provides an exploratory result: PS disrupts the placental GC barrier through hypermethylation and aberrant expression of GC-related genes, resulting in high corticosterone level in offspring, affecting growth and development. However, the molecular mechanism by which PS regulates the placental GC barrier remains to be further determined.

Prenatal stress

DNA methylation

placental glucocorticoid (GC) barrier

placenta glucocorticoid-related genes

Pregnancy is a physiological and widely accepted natural event that women will experience in their life and is considered as a lasting stressful life event. Pregnant women are in various sources of stress, such as environmental toxins, family conflicts, work stress, mental trauma and so on, the psychological state during pregnancy are disturbed by excessive stress. Many epidemiological and case-control studies show that the psychological and social stress of mothers during pregnancy (prenatal stress, PS) not only increases the health risks of the mothers(Glover, 2020; Gu & Guan, 2021), but also is associated with risk of experience negative pregnancy outcomes including abortion, premature birth and low birth weight as a result of exposure to prenatal stress(Fleming et al., 2018; Londono Tobon et al., 2016; Morris et al., 2022; Rosa et al., 2019). As noted, PS lead to anxiety during pregnancy has been implicated as an important risk factor for the abnormal health and emotional disorders and depression behavior in offspring(Clayborne et al., 2022). Therefore, PS should get the full attention of all society.

Currently recognized fetal programming was first proposed in the seminal works of Barker and Hales(Godfrey & Barker, 2001; J., 1990), who put forward pre-birth events can constantly modify and change the health imprints left by our ancestors, leading to an increase in the risk of adult disease through physiologic and metabolic responses changes(Ray, 2020). In accordance to the Barker's hypothesis on Developmental Origins of Health and Disease (DOHaD), it’s particularly worth pointing out that the risk of cognitive, neurodevelopmental impairment and mental disorders of offspring will occur as a consequence of PS(Djp, 2004). The abundance of PS has been found to cause negative programming and structural, physiologic, metabolic,and epigenetic changes in the offspring, as demonstrated by many epidemiological studies (Fransquet et al., 2022; Khurana et al., 2019; Lamothe et al., 2020; Van den Bergh et al., 2020).

There is currently a lot of attention on the relationship between stress exposure during pregnancy and adverse health effects, but the mechanisms of PS transmission are unknown. Cortisol is a well-known stress hormone that is primarily responsible for acting as the primary glucocorticoid (GC) hormone in mammals. The fetus is protected from excess synthetic GC exposure by almost all of mammal’placentas, including humans, which serve as a barrier. Choi et al have shown that cortisol in pregnant women experiencing high stress can damage the placental GC barrier, which is a widely recognized mechanism(Choi et al., 2022). Under a state of PS, the sustained activation of the hypothalamic-pituitary-adrenal (HPA) axis can lead to the subsequent secretion of large amounts of GCs and the dysfunction of the nervous, endocrine, and immune systems, among others(Ruffaner-Hanson et al., 2022). Notably, the development of chronic diseases in later life is linked to significant impairment of the GC barrier in the first trimester of life and to intrauterine growth retardation(Zhu et al., 2019).

In accordance to the current study, Vuppaladhadiam et al. have demonstrated that PS increases the correlation between plasma and placental cortisol levels(Vuppaladhadiam et al., 2021), and it has also been suggested that prenatal stress may reduce child growth and disease through epigenetic regulation of placental susceptibility—possibly affect epigenetic programming of GC-related stress gene pathway. The primary regulators of maternal-fetal GC levels are 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) and p-glycoprotein (P-gp). 11β-HSD2 primarily catalyzes GC, converting it from active to inactive, thereby maintaining an appropriate GC during pregnancy(Ji. et al., 2022; Vuppaladhadiam et al., 2021). The ATP-binding cassette family of placental efflux transporters contains a protein known as P-gp that is very expressed. Placental trophoblast P-gp can utilize ATP to restore GC in the maternal stream(Ge et al., 2021; Tang et al., 2023). Many studies on different animals including humans have revealed that DNA methylation of stress-related HPA axis genes is one potential epigenetic mechanism by which chronic stress may alter GC sensitivity (Florian Rakers; Ji. et al., 2022).

Glucocorticoid receptors (GR) play a significant role in the HPA axis, a neuroendocrine system that coordinates stress responses. In several studies, PS was found to cause alterations in the expression of the GR gene (also known as Nuclear Receptor Subfamily 3 Group C Member 1, or NR3C1) and FK506 binding protein 5 (FKBP5), which are a major regulator of the stress response(Monk et al., 2016; Muller et al., 2022). Hippocampal NR3C1 and FKBP5 in the negative feedback regulation of the HPA axis function, and are indispensable factors in the regulation of the stress response and plasticity of the hippocampus. It has been suggested that the effects of stress on NR3C1 promoter methylation serve as an intermediate process by which environmental stimuli result in stable modifications of the phenotype and shape individual responses to stress(Appleton et al., 2015; Conradt et al., 2013; Taguchi et al., 2023). Maternal depression is associated with a higher degree of placental DNA methylation of NR3C1 (gene encoding GR), which indicates that newborns have poor self-regulation ability, low muscle tone and sleepiness(Chalfun G; Wei et al., 2022). While other reports have demonstrated that maternal adversity (depression, low socioeconomic status) is associated with high levels of NR3C1 mRNA in the placenta(Mansell et al., 2016; Yoshino et al., 2022). Moreover, the offspring of the rat exposed to low levels of maternal care have been reported to have higher levels of NR3C1 methylation compared to offspring exposed to more maternal care(Turecki & Meaney, 2016). The NR3C1 gene is a crucial player in the process of promoting stress and addictive behaviors, as highlighted in these studies. The is a lack of clarity on how the epigenetic regulation of placental GC barrier related genes affects prenatal stress and results in higher GC.

Therefore, we used the Chronic Unpredictable Mild Stress (CUMS) model to generate a rat model of PS by applying varying stressors at unexpected times during pregnancy. As well as, an assay of the HPA axis (corticosterone) in relation to DNA methylation of four GC-related placenta genes (11β-HSD2, P-gp, NR3C1, and FKBP5) using RRBS gene sequencing technology and the MethylTarget™ assay (targeted bisulphite sequencing PCR). We hypothesized that PS would elevate corticosterone which was associated with these four GC-related placenta genes based on the abnormal placenta DNA methylation, so as to explore the mechanism of maternal and offspring’s high corticosterone caused by pregnancy stress.

2.1 Chemicals and reagents

Iodine-131 cortisol radioimmunoassay kit (Beijing North Institute of Biotechnology). RNA extraction kit, First strand cDNA synthesis kit and real-time PCR reaction kit (Beijing Tiangen Biochemical Technology Co., Ltd.). RIPA lysis solution (Nanjing KGI Biotechnology Co., Ltd.). Whole protein extraction kit, bicinchoninic acid (BCA) protein content detection kit. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel preparation kit. Substrate chemiluminescence (electro-chemi-luminescence, ECL) kit. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel preparation kit, electro-chemi-luminescence (ECL) kit (Nanjing KGI Biotechnology Co., Ltd.). Primary antibodies anti-β-actin (AF7018), anti-DNMT 3A (DF7226), anti-DNMT 3B (AF5493), anti-DNMT1 (DF7376) and Goat-Rabbit IgG (S0001)were purchased from Affinity Technology Co., Ltd. The anti-11β-HSD2 were purchased from Novus Technology Co., Ltd. (NBP1-39478). The anti-P-gp (ab170904) and anti-NR3C1 (ab183127) were purchased from Abcam Technology Co., Ltd. The anti-FKBP5 (00083356) was purchased from Proteintech Technology Co., Ltd.

2.2 CUMS Procedure

Sixteen female adult healthy Sprague- Dawley (SD) rats weighing (200 ± 20)g and twelve male Sprague Dawley (SD) rats weighing (220 ± 20)g were obtained from the Experimental Animal Center of Ningxia Medical University(Experimental animals certificate number: SYXK(Ning)2020-0001). All procedures were approved by the Ningxia Medical University Animal Care and Use Committee and were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (IACUC-NYLAC-2020068). Rats were kept in cages in controlled temperature (22 ~ 24℃) and light (12h light/dark cycle) conditions. Food and water were readily available throughout the study, except for certain experimental procedures. Rats were accustomeded to the laboratory conditions for at least a week before in the experiments. We made every effort to reduce the amount of pain and the numbers of the animals used in these experiments.

Sixteen female rats were randomly divided into a prenatal stress group (PS group) and a prenatal control group (PC group), with each group consisting of eight rats. Twelve male rats were randomly divided into a control mating group (four rats) and a stress mating group (eight rats). The female rats in the prenatal stress group reared in individual cages, while the prenatal control group had four female rats in each cage; the male rats reared in an environment with three rats per cage. The prenatal stress group performed mating from the 7th day after being subjected to chronic unpredictable mild stress. Before gestation, PS group mated with a stress mating group male in one cage, two rats in PC group were mated with a control mating group male in a cage. The vaginal walls of all female rats were tested every morning to confirm pregnancy by sperm positivity, designated gestational day 0(GD 0), then the male and female rats were separated.

After gestation, they were forced to return to the original breeding environment. PC group rats were housed three in every cage (1 per cage after GD 18), while PS group rats was housed individually in a separate room. When the rats were mating, the PS group rats were subjected to uninterrupted stress stimulation. All rats were raised in standard experimental conditions. To recreate chronically stressful conditions, the chronic unpredictable mild stress (CUMS) procedure was on the basis of a previously described method of minor amendments and supplements (Ayuob et al., 2021; Guan et al., 2021). PS group rats were exposed to one of seven different stressors was randomly chosen to stimulate daily for 4 weeks. A variety of stressors were used including crowded environments for 24 h, physical restraint (2h), soiled cage for 24 h (60 ~ 70 percent humidity, 12h), hot water swimming (at 45, 5 min), rocking cages ( 30 min); ice bath (at 4℃, 5 min); squeezing tail for 2 min. One of the seven different stressors was randomly administered each day. 24-hour stressors were begun at 10: 00 in the morning to 10: 00 the next morning; and all other stressors were applied between 10: 00 to 12: 00 in the morning. During the process, the stressed rats were moved into another room (The light intensity and temperature of two rooms were the same), and then back to the main room after the stimulation.

2.3 Placental sampling

After gestation the eighteenth day, the female rats were anesthetized with isoflurane (three in the PS group and three in the PC group), and the uterus tissues were quickly removed. The embryo mixture of the blastocyst was put into precooled PBS solution in advance and the uterus and fetal membrane tissues were removed, and the placenta was separated. Three placental tissues from two group were frozen immediately in liquid nitrogen, followed by storage at -80°C for subsequent examination.

2.4 Allocation of the offspring into groups

After delivery of the pups (day of birth = Postnatal day, PND 0), the dams and pups were kept together until weaning on postnatal day (PND) 21, when the male and female pups were separated. Ten offspring of prenatal control group (OPC group) and ten offspring of prenatal stress group (OPS group) were randomly selected. They were assigned to two groups: OPC group (n = 10, 5 male vs 5 female), OPS group (n = 10, 5 male vs 5 female), All experimental offspring were reared in the same room with free to water and food. The offspring rats weighed and blood was collected from the inner canthal vein on PND28 and PND42. Schematic representation of the experimental procedure (Fig. 1).

2.5 Measurement of selected indicators

2.5.1 Measurement of plasma corticosterone concentration

The successful establishment of CUMS model was confirmed by measuring the plasma corticosterone concentration of female rats at different stress time points. Blood (1 mL) from inner canthus vein were collected from the rats on the day before first stress (baseline) and then on the 1st, 7rd, 14th, 21st and 28st days during stress. As plasma was needed to determine plasma cortisol levels, blood samples centrifuged at 3000 rpm for 10 min at 4℃, and the plasma drawn off and stored at -80℃. Corticosterone was evaluated using a ¹³¹I cortisol radioimmunoassay (RIA) kit according to the provided instructions of the manufacturing kit. The intra-assay variability of the RIA ranged from 3.2 to 4.7%. The plasma corticosterone levels were identified from the measured cortisol value by the following conversion formula: corticosterone concentration = cortisol concentration × 50(Liu et al., 2004).

2.5.2 Reduced representation bisulfite sequencing

Genomic DNA in two offspring group (three placental samples in OPC group and three placental samplings in OPS group) was extracted using a magnetic universal genomic DNA Kit according to the manufacturer’s protocol and subjected to RRBS library preparation using the Acegen Rapid RRBS Library Prep Kit according to the manufacturer’s protocol.

2.5.3 Illumina Hiseq sequencing platform (Beijing Biomarker technologieb)

RRBS sequencing was performed at Biomarker technologies. First, 100 ng of genomic DNA was digested with MspI, end-repaired, 3'-dA-tailed and lighted to 5-methylcytosine-modified adapters. Genomic DNA was purified with Agencourt® AMPureAcer® XP(Nucleic Acid Purification Kit). Then, ZYMO EZ DNA Methylation-Gold Kit was used only for bisulfite treatment and Illumina 8-BP double index primers were used for 12-cycle PCR amplification. The quality of the library was analyzed using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). And the ABI StepOnePlus real-time PCR system (Thermo Fisher, CA) was used to quantify DNA fragments ranging in size from 25 bps to 12,000 bps. Results of Fragments were sequence using a 150×2 paired terminal sequencing scheme on Illumina NovaseQ6000 analyzer (Illumina Hiseq sequencing platform). After sequencing, methylation analysis were conducted on the promoter and CpG island region by detecting the proportion of CG, CHG and CHH in the methylated C base, the average methylation level of C base and the distribution of different types of methylation. Methylation levels and CpG density were used to calculate regional methylation profiles for specific regions, and CpG density was defined for each CpG site within a 200 bps window. The sliding window method was utilized for differential methylation region (DMR) analysis to calculate the methylation difference between the placental samples of OPC and OPS group.

2.5.4 Validation of DNA methylation status and detection of the expression levels of the identified markers

The DNA methylation status of the P-gp (abcb1a) and FKBP5 intron was analyzed using the MethylTarget™ Assay (targeted bisulphite sequencing), a next generation sequencing based multiple targeted CpG methylation analysis method. Two target genes with the greatest differences in DNA methylation were subject to further analysis (Supplementary Tables1). Six placenta in the OPC group vs six placental in the OPS group) was used to validate the results of RRBS and to detect the expression of these differentially methylated genes. Sample acquisition, DNA extraction and preservation were performed as referred to above.

First, 40 ng of genomic DNA was subjected to bisulphite conversion using the EZ DNA Methylation- Gold™ Kit (Zymo Research) according to the manufacturer's instructions, and PCR was performed to amplify the targeted DNA sequences. The products were sequence on an Illumina MiSeq bench top sequencer. Primer sequences of the target region within the FKBP5 and P-gp (abcb1a) locus were enumerated as follows Tables 1. FastQC performed quality control of the sequencing reads. Filtered reads were assigned to the genome by Blast after read recalibration with USEARCH.

Table 1

Primers in MethylTarget sequencing for validation of DNA methylation status
Gene name	Primer Forward	Primer Reverse
FKBP5_01	TAGTGAGGGAGTATTTGAGAGTATTGTT	AACCTAAAATATTAAAACATTCRAAACTAACC
FKBP5_02	AGTAAGTYGGTTAGTTATTAYGTTAGGATTTT	AACCTCCCTAACCTACAATTCTAATCAA
FKBP5_03	AAAGTATTGAGTAAGAAGGTAGTAGAAGGAG	CTTACCATTAACTTCCAAAATCACAA
FKBP5_04	AGGGATGGTTGTGTATTTAGGTTTTT	TACCACAACCAAAATCCACCAC
FKBP5_05	TATGTTGGGTAAAGTTGGGTTTG	TATCTCAACTTTAATTTCCTCAAAACA
FKBP5_06	GGTATAGTAGGGGTTGGGGATTT	ACAAAATCTCATACCACTAAAACTAACCTT
FKBP5_07	AAATGGAAAGGAGATAAAGAAGTTTG	TCTCCACACAACCCTACTCTACAAA
FKBP5_08	AGTTGGTTGGTTTGGTTAGTGTGAA	AACCTCACCCACTTCTTCATCTTC
abcb1a_09	TGTTATAGTTGTGTAGTTTGGGTGTTT	TCCAAAAATATCAACCAAACATACCAT
abcb1a_10	ATGTGTTAGTGTTTTAGGGGTTGAGTTT	CCAAATAAAATTTCAAACCTTCACTCA
abcb1a_11	TTGGAAGTTAGGTTGTTTAAAATATTGAGA	AATAAAATCATAAACATTAACCTCTTTAACAAC
abcb1a_12	TTTGAAGTATTGTTGAGTAAGGAGAAGGTA	ACTACACTCCTAACAACTAACCCTAACA
abcb1a_13	TTTGTTGTTATGTAGTTGTGTGTTTTT	CTTAACTAACCCTCAATCCAAACATAA
abcb1a_14	TTGYGTTGTAGGTTGTTGGTAGTTTGTT	ATTAACCACAACTAATAACRTACAAACCTA
abcb1a_15	TTTGGTTATGTATTTAGGAGTATAGTAGGTTTTT	CCCCTTCCCCATCCTACC
abcb1a_16	ATGTTTATAGGTGGAATTAAGTGTGTTTT	TTCTAAATACCCATATAACATCTCACACC
abcb1a_17	TGGATTTTTATTTGTATTGTTGTTGTAG	ACTCCTTCCCTAACTCCCTATTTTT
abcb1a_18	TAGGGTTTGGGAGTAGAGAGTTGTT	AAAAATCCAATAAATCCCACAAAAAC
abcb1a_19	GAAAGTTGTTAAGGAAGTTAATGTTTATG	TCTCAATTCACRTCTCRAACTAAAAA
abcb1a_20	TTTTTAGTGTAGATATAGGGGAGTGTTG	AACCAACCTATCTCCTAATTCATAATAACA
abcb1a_21	TTAAGTGTTGGGATTGTTATGTTTG	AACAATCCRTCTAAAACTACAACCACTAA
abcb1a_22	AAGGTTAATATTGAAGGGGTTGTATTT	CTCAAAACCCCACAAAACCT
abcb1a_23	ATGTGGGTTTTGGGTGTGAA	AAACCCACCATAAATCCTTCCTAA
abcb1a_24	GTTTTTAAGATAGAATTTATTTGAGGTTAGAT	AAACCAATCCTATATAATCATCCCCATT
abcb1a_25	AAYGAGGTTAAATAAGTTTGAGTTTGTATT	CTTACATACCATACATTCTAAAACAAAACA
abcb1a_26	TTAGAGAGAAGGATTGGTTAGAAAATAGTTA	CTAACTTAAACATAACACRAAATACTCACTCTA

2.5.5 Quantitative real-time PCR (qRT-PCR)

RNA was extracted from isolated placenta tissues according to the instructions of the “RNA simple Total RNA” kit. Extracted RNA quantified using a nucleic acid protein quantitative instrument. Samples with an A260/280 ratio of between 1.8 ~ 2.0 were used. Briefly, after homogenization of the tissue in reaction mixture containing RNA-Bee and chloroform, and centrifugation at 12,000g for 10 min at 4℃, the extracted RNA in the aqueous phase was achieved. 200 mL of this aqueous phase was combined with the same volume of absolute ethyl alcohol and after centrifugation at 12,000g for 30s at 4℃; the RNA precipitated and formed a pellet. The pellet washed with 75% ethanol (by overtaxing and centrifugation at 7,500g for 5 min at 4°C), the ethanol removed and the pellet of RNA allowed to dry. The pellet was stabilized in 25µL DEPC water. RNA concentration and purity evaluated by spectrometry utilizing optical density (OD) measurements between 260–280 nm. A two-step Real-time Quantitative PCR (RT-qPCR) assay was performed. The cDNA was obtained from 1000ng placental tissue of total RNA in a total volume of 20 µL using a cDNA synthesis kit. PCR was performed using the SuperReal PreMix Plus (SYBR Green)reagent for performing real time PCR assays for the genes encoding DNMT 3A, DNMT 3B, DNMT1, 11β-HSD2, abcb1a, abcb1b, NR3C1 and FKBP5 RT-PCR primers used were as follows in Table 2. The specific conditions for the PCR reaction were as follows: pre-denaturation 15min at 95°C, denaturation 10s at 95℃, annealing 20s at 55℃, and extension 30s at 70℃, for 40 cycles. The 2-ΔΔCt method was used to calculate the relative expression level of the target gene.

Table 2

**Primers in qRT-PCR for detection of gene transcription**
Gene name	Primers forward	Primers reverse
DNMT 3A	CGTCACACAGAAGCATATCCAGGAG	CAGGAGGCGGTAGAACTCAAAGAAG
DNMT 3B	GATGGAGATGGTGAAGCGGATGATG	AGGCTGGAGATACTGTTGCTGTTTC
DNMT1	TGTTCCTCCTTCTGCCATCAATGTG	CATCGTCCTTAGCGTCGTCGTAAC
11β-HSD2	CCTCTGGACCTCTCCTCTGCTTC	GCCACATCTCACGCTAAACTCTCC
abcb1a	GAAGAAGACCTTAACGGAAGAGCAGAC	GCGAAACATTGTGAGCACACTGAC
abcb1b	CTCGCTGCTATCATCCACGGAAC	CGCTGACGGTCTGTGTACTGTTG
NR3C1	CACATCTCACCCGCACCGATTG	TTGGACAAACACGGATGCCTGAC
FKBP5	TGGTGGGCATGGCAGGGATC	GCTTGTGTCAGGCGAGTCAGTG
β-actin	TGTCACCAACTGGGACGATA	GGGGTGTTGAAGGTCTCAAA

2.5.6 Western Blot Analyses

Intact protein was extracted from placenta tissue using a cold Radioimmunoprecipitation assay (RIPA) lysis buffer system. The protein concentration of the samples was determined with a BCA assay kit. Equal amounts of protein were separated by SDS-PAGE at 10% concentration, and then transferred onto a PVDF micro porous membrane. The membranes were blocked with 5% skim milk and then incubated with anti- DNMT1 (1:1000), anti-DNMT 3A (1:1000), anti-DNMT 3B (1:1000), anti-1β-HSD2 (1:2000), anti P-gp (1:2000), anti- NR3C1 (1:2000), anti- FKBP5 (1:2000), or anti-β-actin Protein antibody (1:2000) at 4°C overnight,, followed by incubation with secondary antibodies at room temperature for 1 h. The blots were visualized by ECL kit. Immunoreactivity was detected using ECL Plus detection reagent (Jiangsu Kaiji Biotechnology Co., LTD).

2.6 Statistical analyses

Statistical analysis of all data, expressed as mean ± SD, which was performed using of Statistical Package for the Social Sciences (SPSS) 26.0, and all graphs were constructed in GraphPad Prism 8.0. Differences between the means of prenatal data in plasma corticosterone level was analyzed using repeated measurements analysis of variance and Student’s t test to make multiple comparisons at different times. All of offspring RT-PCR and Western blotting data were analyzed using Student’s t test for comparison between the two offspring groups. P < 0.05 was examined statistically significant.

3.1 CUMS increases prenatal plasma corticosterone levels

Repeated-measures ANOVA showed a significant effect of chronic stress on prenatal corticosterone concentration (F = 7.632, P = 0.020), with a larger change in corticosterone concentration in the PS group with increasing duration of stress ((F = 6.871, P = 0.001). Also, there was a significant interaction between stressors and time ((F = 3.141, P = 0.015). The plasma corticosterone concentrations in the PS group were higher than in the PC group at days 14, 21 and 28 of stress (t₁₄=-2.811, P = 0.018; t₂₁=-3.398, P = 0.007; t₂₈=-3.060, P = 0.012). The results suggested that the PS group were in a state of stress during pregnancy. Plasma corticosterone concentration were not statistically significant between the PC and PS groups at baseline examination (t=-1.607, P = 0.139) and on day one after exposure to stress (t=-0.359, P = 0.727) (Fig. 2).

3.2 The effects prenatal stress on offspring’s body weight and plasma corticosterone

Repeated measure analysis of variance revealed significant differences between corticosterone concentration in OPS group and the OPC group (F = 20.588; P = 0.000), with a larger change in corticosterone concentration in the OPS group with increasing of birth time (F = 8.783; P = 0.008). No significant differences were noted in interaction between treatment group time (F = 0.903; P = 0.355). The plasma corticosterone concentrations in the OPS group were higher than in the OPC group at PND28 (t =-2.537; P = 0.027, Fig. 3A) and PND42 (t=-3.764; P = 0.003, Fig. 3B). The results suggested that the OPS group were in a state of elevated plasma corticosterone level.

Repeated measures by ANOVA of the two groups highlighted significant effect on offspring body weight (F = 14.384, P = 0.001). Meanwhile, there was significant time effect but no significant difference in terms of interaction between time and group for body weight (Time: F = 641.390, P = 0.000; Interaction: F = 0.750, P = 0.398). The body weight in the OPS group were lower than in the OPC group at (t = 3.645; P = 0.002, Fig. 3C) and PND42 (t = 3.044; P = 0.008, Fig. 3D).

3.3 The effects of offspring after prenatal stress during pregnancy on enzymes of DNA methylation (DNMTs)

As shown in the Fig. 4, the mRNA expression of DNMT 3A, DNMT 3B and DNMT1 differed between the two offspring groups were shown in Fig. 4A ~ C (all P < 0.05). Compared with the OPC group, the mRNA levels of DNMT 3A (t = 3.997, P = 0.003, Fig. 4B) and DNMT 3B (t = 3.210, P = 0.009, Fig. 4C) were markedly down regulated and the mRNA expression of DNMT1 (t= -2.401, P = 0.037, Fig. 4A) was up regulated in the PS group. The protein levels of DNMT 3A (t = 8.443, P = 0.001, Fig. 4D, F) and DNMT 3B (t = 5.610, P = 0.005, Fig. 4D, G) were down regulated in the placenta of the OPS group, while the protein levels of DNMT1 were up regulated (t=-3.601, P = 0.023, Fig. 4D, E). Overall, indicating that prenatal stress induced placenta enzymes of DNA methylation change in the offspring.

3.4 DNA methylation status in Placenta

RRBS was performed using placental tissues to identify differentially methylated CpG sites and regions. After quality control and data analysis, 222119 differentially methylated sites and 6905 DMRs identified between the two groups. 4055 DMRs were hyper methylated and 2850 hypomethylated in the OPS group compared to the OPC group. Of these, 110,752 sites had gene names, 59,778 sites were stronghyper, and 50,974 sites were stronghypo in methylation levels compared to the OPC group. Importantly, These differentially methylated sites form 6905 differentially methylated regions, of which 2850 have hyper methylation and 4055 have hypo methylation levels. Tables 3 show gene DMR CpG sites and target in the OPC and OPS groups.

Table 3

Statistics of differentially methylated region in all differentially methylated gene
Different methylation region	Gene numbers
Promoter	261
5’UTR	60
Exon	1219
3’UTR	121
intron	1648
Distal Intergenic	3509
Downstream	87
Total	6905

From all the genes with identified DMRs, there was for two gene associated with placental GC barrier function in DMR: P-gp (abcb1a) and FKBP5. Among groups, P-gp(abcb1a) were thirty-two differentially methylated sites. Compared to OPC group, 8 sites were hypermethylated and five sites were hypomethylated in the exon and twelve sites were hypermethylated and seven sites hypomethylated in intron in the OPS group. The differentially methylated sites formed two differentially methylated regions, located at exon and Intron regions, both with hypermethylated methylation levels. FKBP5 were eleven differentially methylated sites(all P < 0.05), five sites were hypermethylated and three sites were hypomethylated in the intron, and one sites were hypermethylated in upstream and two sites hypermethylated in downstream. See Table 4 ~ 5.

Table 4

Statistics of differentially methylated sites in placental GC barrier related genes
Gene	Postion	Meth_direction	P value	Annotation
FKBP5	7975113	strongHyper	0.000388	downstream
FKBP5	7975116	strongHyper	0.00181	downstream
FKBP5	7977241	strongHypo	0.00809	intron
FKBP5	7977273	strongHyper	0.00441	intron
FKBP5	7978647	strongHypo	0.000072	intron
FKBP5	7991860	strongHypo	0.000641	intron
FKBP5	7991871	strongHypo	0.00288	intron
FKBP5	7992251	strongHyper	0.00125	intron
FKBP5	8000100	strongHypo	0.000573	intron
FKBP5	8002228	strongHyper	0.000295	intron
FKBP5	8019250	strongHyper	0.00239	upstream
abcb1a	22140561	strongHyper	0.00356	intron
abcb1a	22140572	strongHyper	0.00524	intron
abcb1a	22141030	strongHyper	0.00142	exon
abcb1a	22141031	strongHyper	0.000573	exon
abcb1a	22141043	strongHyper	0.0057	exon
abcb1a	22141058	strongHyper	0.000483	exon
abcb1a	22141069	strongHyper	0.000151	exon
abcb1a	22141070	strongHyper	0.000373	exon
abcb1a	22165829	strongHypo	0.0000386	exon
abcb1a	22181973	strongHyper	0.000137	intron
abcb1a	22181974	strongHyper	0.000138	intron
abcb1a	22182070	strongHyper	0.00111	intron
abcb1a	22182101	strongHyper	0.00212	intron
abcb1a	22192599	strongHypo	0.00392	intron
abcb1a	22222671	strongHypo	0.00295	intron
abcb1a	22222675	strongHypo	0.00000464	intron
abcb1a	22234557	strongHyper	0.00000554	intron
abcb1a	22234706	strongHyper	0.0022	intron
abcb1a	22255279	strongHyper	0.00128	intron
abcb1a	22279931	strongHypo	0.00478	intron
abcb1a	22297010	strongHyper	0.0073	exon
abcb1a	22297022	strongHypo	0.00601	exon
abcb1a	22304484	strongHyper	0.00327	intron
abcb1a	22304577	strongHyper	0.00623	intron
abcb1a	22313828	strongHypo	0.00286	intron
abcb1a	22320455	strongHyper	0.00229	intron
abcb1a	22322568	strongHypo	0.00142	intron
abcb1a	22322825	strongHypo	0.000596	intron
abcb1a	22397453	strongHypo	0.00429	exon
abcb1a	22397481	strongHypo	0.000487	exon
abcb1a	22397482	strongHyper	0.0000549	exon
abcb1a	22397592	strongHypo	0.0018	exon

Table 5

Statistics of differentially methylated region in placental GC barrier related genes
Gene	Start	End	Meth_direction	MethDif	P value	Annotation
abcb1a	22141030	22141072	strongHyper	0.225	0.00000000156	Exon( exon 25 of 28)
abcb1a	22181973	22182103	strongHyper	0.244	0.000000000029	Intron(intron 27 of 27)

3.5 Effect of prenatal stress on DNA methylation of GC related genes in placenta of offspring

To determine whether the placental GC barrier related gene P-gp (abcb1a) and FKBP5 gene was differentially expression in OPC group and OPS group, its DNA methylation level further confirmed by Methyltarget sequencing. Primers used for MethylTarget validation and qRT-PCR are given in Table 1.

In total, twelve placental samples from six OPC group and six OPS group of GD18 were used only for the validation experiments. Table 6 and Fig. 5 displays P-gp (abcb1a) in the OPS group had 15 differentially methylated CpG sites and three differentially methylated fragments (abcb1a DMR04, abcb1a DMR05 and abcb1a DMR17) were significantly hyper methylated compared to the OPC group. FKBP5 in the OPS group had 15 differentially methylated CpG sites and two differentially methylated fragments (FKBP5 DMR3 and FKBP5 DMR4) were markedly hyper methylated compared to the OPC group.

RRBS and validation results revealed that compared to the OPC group, three same CpG methylation sites(abcb1a DMR4 chr4: 22182070、chr4༚22181973 and abcb1a DMR5 chr4༚22182070) were consistent with RRBS results. Then, Contrast the position of CpG sites in each fragment found that nine differential methylation sites had the same methylation direction in both tests. Likewise, there were sixteen differential methylation sites in FKBP5, which were different in the two tests, and the methylation directions of three differential methylation sites were the same. Moreover, FKBP5 gene was total significance and was hyper methylation level (Fig. 5). Further studies found that, the consequences of RRBS and MethylTarget were significant differences in methylation levels of P-gp (abcb1a) and FKBP5. Moreover, Methylation position were distributed in the intron region, suggesting that PS may have an impact on methylation levels in the intron region of P-gp (abcb1a) and FKBP5.

Table 6

Differences of methylation levels in specific genes CpG site between different groups
Target	Position	Chr	Genome	Meth_direction	t-value	P-value	OPS()	OPC()
FKBP5 DMR2	161	20	7977242	strongHyper	2.567	0.047	0.755 ± 0.008	0.707 ± 0.045
	201	20	7977202	strongHyper	3.278	0.014	0.939 ± 0.005	0.922 ± 0.012
	230	20	7977173	strongHyper	5.425	0.000	0.874 ± 0.008	0.841 ± 0.012
FKBP5 DMR3	32	20	7978764	strongHyper	2.810	0.019	0.596 ± 0.075	0.485 ± 0.060
	36	20	7978760	strongHyper	3.113	0.016	0.509 ± 0.082	0.392 ± 0.042
	48	20	7978748	strongHyper	3.003	0.018	0.450 ± 0.078	0.342 ± 0.042
	130	20	7978666	strongHyper	2.738	0.022	0.361 ± 0.045	0.282 ± 0.054
	149	20	7978647	strongHyper	2.482	0.037	0.428 ± 0.049	0.335 ± 0.078
FKBP5 DMR4	178	20	7991872	strongHyper	7.129	0.000	0.880 ± 0.014	0.831 ± 0.010
FKBP5 DMR4	189	20	7991861	strongHyper	2.996	0.028	0.884 ± 0.007	0.835 ± 0.040
FKBP5 DMR5	63	20	7992159	strongHyper	3.353	0.010	0.683 ± 0.023	0.623 ± 0.038
FKBP5 DMR5	200	20	7992296	strongHyper	2.247	0.052	0.392 ± 0.036	0.333 ± 0.054
FKBP5 DMR6	54	20	8000070	strongHyper	3.700	0.011	0.959 ± 0.002	0.947 ± 0.007
FKBP5 DMR7	48	20	8002300	strongHyper	1.934	0.092	0.818 ± 0.015	0.791 ± 0.031
	81	20	8002267	strongHyper	3.414	0.007	0.936 ± 0.008	0.920 ± 0.008
	156	20	8002192	strongHyper	2.409	0.050	0.701 ± 0.652	0.604 ± 0.085
abcb1a DMR04	91	4	22181973	strongHyper	2.639	0.030	0.099 ± 0.038	0.052 ± 0.021
abcb1a DMR04	188	4	22182070	strongHyper	2.554	0.032	0.091 ± 0.027	0.057 ± 0.018
abcb1a DMR05	74	4	22182070	strongHyper	2.907	0.018	0.088 ± 0.030	0.045 ± 0.020
	141	4	22182137	strongHyper	2.272	0.046	0.170 ± 0.047	0.109 ± 0.046
	156	4	22182152	strongHyper	2.872	0.017	0.260 ± 0.062	0.165 ± 0.052
	209	4	22182205	strongHyper	2.688	0.031	0.079 ± 0.034	0.038 ± 0.016
abcb1a DMR06	49	4	22192666	strongHyper	2.743	0.023	0.013 ± 0.002	0.010 ± 0.002
abcb1a DMR06	164	4	22192551	strongHypo	-3.106	0.012	0.010 ± 0.003	0.014 ± 0.002
abcb1a DMR10	76	4	22255365	strongHypo	-13.949	0.000	0.056 ± 0.091	0.671 ± 0.0581
abcb1a DMR13	58	4	22304610	strongHyper	2.369	0.050	0.420 ± 0.034	0.342 ± 0.074
abcb1aDMR14	35	4	22313874	strongHyper	2.573	0.041	0.163 ± 0.022	0.139 ± 0.008
abcb1a_DMR15	55	4	22320554	strongHyper	4.918	0.001	0.354 ± 0.033	0.211 ± 0.063
abcb1a DMR17	115	4	22322775	strongHyper	2.694	0.026	0.668 ± 0.052	0.601 ± 0.033
	154	4	22322814	strongHyper	2.371	0.043	0.223 ± 0.053	0.128 ± 0.083
	232	4	22322892	strongHyper	2.808	0.029	0.181 ± 0.062	0.105 ± 0.024

3.6 Effect of prenatal stress on transcription of the placental GC related genes of offspring

To figure out the mRNA and protein expressions of genes related to placental GC barrier 11β-HSD2, P-gp, NR3C1 and FKBP5, we collected placental samples of OPC group and the OPS group. It was further found that the mRNA expression levels of placental 11β-HSD2 (t = 2.880, P = 0.016, Fig. 6A) and P-gp ( abcb1b )(t = 7.874, P = 0.000, Fig. 6C) were significantly reduced in the OPS group compared with the OPC group. However, The mRNA levels of P-gp( abcb1a ) (t = 2.036, P = 0.081, Fig. 6B) in the OPS group was not statistically significant compared with the OPC group. Interestingly, the same trend was observed at the translation levels of 11β-HSD2 (t = 5.667, P = 0.030, Fig. 6F, J) and P-gp (t = 10.542, P = 0.009, Fig. 6F, I). Significantly, exposure of offspring to GC induces increased in FKBP5 mRNA levels (t=-2.428, P = 0.036, Fig. 6E), whereas NR3C1 mRNA levels were down regulated (t = 2.729, P = 0.032, Fig. 6D). In addition, the protein expressive level of NR3C1 was higher than that of OPC group (t=-73.099, P = 0.000, Fig. 6F, I), and the expression level of FKBP5 protein was lower than that of OPC group (t = 12.061, P = 0.007, Fig. 6F, G). These data together indicate that maternal stress increased the corticosterone concentration in the offspring by weakening placenta GC barrier function.

Pregnant women and developing fetuses are in a unique period and are especially susceptible to external environment. An important plastic stage of tissue and organ development occurs in the uterus. The fetus continuously absorbs nutrients oxygen, various hormones, and other crucial factors from the mother in the process of growth and development(Demers et al., 2021). In addition, placental trophoblasts are tightly bound and act as a protective barrier to maintain the stability of the intrauterine environment. When external pollutants enter the placenta through maternal blood, they are absorbed by the trophoblast and become toxic, thereby disrupting the placental barrier and nutrient function(Franks et al., 2019). Continued stress during pregnancy activates the HPA axis, which in turn stimulates the production of large amounts of cortisol (corticosterone in rodents)(Chen et al., 2019). The present study confirmed the findings about the plasma corticosterone concentrations in the prenatal stress (PS) group were higher than in the prenatal control group at different days of stress(Zhu et al., 2019), which is an important finding in the understanding of being activated of HPA axis after PS(Lais Fajersztajn, 2017). Excessive corticosterone as harmful substances may go into the uterus through placental barrier, and then exposing the fetus to high levels of corticosterone, it was also clear supported by the results of our experiment.

As noted in the research background, all placentas of mammals are equipped with glucocorticoid (GC) barriers to protect the fetus from the adverse effects of overexposure to cortisol. External stressors can further activate the placental maternal HPA axis in a manner consistent with the classic endocrine response, leading to the production of GC(Reynolds et al., 2013). However, whether these pathways include the regulation of GC synthesis is unknown. Therefore, an important part of this study is how PS affects the GC carrier barrier and the components of the GC carrier barrier. Placental GC barrier includes hormone binding proteins, 11β-HSD2 and P-gp(Monk et al., 2016). In this study, we found that the mRNA and protein levels of 11β-HSD2 and P-gp were down regulated in PS group. Consistent with previous studies, ischemia-hypoxia exposure during pregnancy reduces placental 11β-HSD2(Capron et al., 2018; O'Donnell et al., 2012; L. Yu et al., 2018). Exposure to nicotine, stressed marijuana, and ethanol dystrophy significantly reduces P-gp expression in placental trophoblast cells and inhibits its efflux(Ge et al., 2021; Liao et al., 2017), thereby impairing placental GC barrier function, resulting in elevated maternal-to-fetal GC levels. Offspring are sensitive to growth retardation and various chronic diseases (eg, metabolic and neuropsychiatric disorders) in adulthood(Denizli et al., 2022).

The activation of glucocorticoid receptor (GR) is also crucial for individuals to cope with stress. After GR binds with cortisol, it will transfer into the nucleus and act as a transcription factor or inhibitor. Some studies had shown that maternal GC can inhibit the functional development of HPA axis in fetal rats by regulating the expression of GR in hippocampus, and change the intrauterine programming effect of HPA axis(De Alcubierre et al., 2023; Franks et al., 2019), which is manifested in the increased sensitivity of HPA axis to stress response in adulthood. We had verified that using PS produces similar results, the mRNA levels of FKBP5 were up regulated in the PS group; however, the protein level was less than control group. In addition, the mRNA levels of NR3C1 were down-regulated and the protein level was greater than in the PS group. Even though, we did not replicate the previously reported, our results suggested that stress during pregnancy leads to abnormal expression of GR in placenta.

A number of studies on human and animal models have shown that excessive stress during pregnancy, high cortisol, destroys the placental GC barrier and exposes the fetus to elevated GC levels(Alexander et al., 2012; Zhu et al., 2019). The main reason is that maternal corticosterone can persistently activates the hypothalamic axis, interferes with fetal hypothalamic axis development during periods of vulnerability, and persists or permanently affects postnatal hypothalamic axis activity, which may lead to changes affecting mood and emotional development in child cognitive future generations. We define it as a disease of fetal origin based on the hypothesis of intrauterine programming of the HPA axis(Franks et al., 2019; He et al., 2022). Epigenetic modification can change the synthesis and functional formation of placental associated proteins without changing the DNA nucleotide sequence. This function can be influenced by external environmental factors, leading to the occurrence of placental related diseases by regulate placental development and adaptive changes(He et al., 2022; Lawless et al., 2023). DNA methylation may be more susceptible to environmental impact and is a potential prenatal planning mechanism in embryonic stage, which plays a very important role in gene expression, embryonic development and other life processes. Studies also showed that the short and long-term effects of PS on brain structure and function were mediated by epigenetic mechanisms(McGill et al., 2022; Van den Bergh et al., 2020). The results of this study showed that the relative expression levels of methyltransferase DNMT 3A and DNMT 3B mRNA and protein in placentas of PS decreased, while the relative expression levels of DNMT 1 mRNA and protein increased, suggesting that hyper-corticosterone induced by PS was related to DNA methylation.

Corroborating prior studies(Fransquet et al., 2022; Muller et al., 2022), we wanted to prove ectopic expression of GC-related genes due to PS was associated with increased DNA methylation of 11β-HSD2, P-gp, FKBP5, NR3C1 in the placenta. On the basis of RRBS results, MethylTarget technology was used to verify the methylation level of cytosine in all CpG dinucleotides in the target region of candidate genes for the differential methylation sites and regions of placental GC barrier related genes P-gp (abcb1a) and FKBP5. The validation results showed that there were group differences in multiple CpG loci in P-gp (abcb1a) and FKBP5 genes. There were fifteen differential methylation sites in P-gp (abcb1a) gene, which were statistically significant in both tests. Among them, nine differential methylation sites and two differential methylation fragments had the same methylation direction in both tests. In an IUGR research mentioned that the epigenetic regulation of P-gp might play the critical role in mediating the opening of the placental GC barrier(Ge et al., 2021). This study revealed for the first time the fundamental role of P-gp in mediating the placental GC barrier through DNA methylation regulation, and provided a new idea for exploring the mechanism of PS-induced cognitive impairment in offspring. However, the underlying mechanism of epigenetic regulation of placental GC barrier by P-gp remains an important unsolved problem. In addition, there were sixteen differential methylation sites in FKBP5, which were different in the two tests, and the methylation directions of three differential methylation sites were the same. It is noteworthy that higher maternal distress also was associated with placental DNA methylation of FKBP5, which in turn predicted reduced fetal coupling(Monk et al., 2016; Wiley et al., 2023). One additional report has related higher placental DNA methylation of FKBP5 to increased arousal in newborns(Binder, 2009; Paquette et al., 2014). FKBP5 decreases the binding of cortisol to its receptor, leading to decreased cortisol responses, thus it may be that increased FKBP5.

Increased DNA methylation of 11β-HSD2 leads to a down regulation of associated placental mRNA and its encoded protein, barrier enzymes at inactivates cortisol (Ji. et al.). Taken together, great number of findings suggest that maternal prenatal distress alters placental regulation of fetal cortisol exposure via increased DNA methylation of 11β-HSD2, resulting in a fetal risk phenotype of reduced coupling, and after controlling for 11β-HSD2 DNA methylation, greater maternal perceived stress had a direct and positive association with fetal coupling level(Jensen Pena et al., 2012; Ji. et al., 2022; P. Yu et al.). It has been noted that adverse environment during pregnancy can up regulate placenta 11β-HSD2 promoter region H3K9me2 and H3K9Ac level, thereby inhibiting 11β-HSD2(Majchrzak-Celinska et al., 2017; L. Yu et al., 2018). Population epidemiological study found that the methylation status of 11β-HSD2 was substantially correlated with birth weight, gestational age at delivery, maternal age, body mass index and hormone level(Dwi Putra et al., 2017; Jahnke et al., 2021). In a clinical trial, it was found that the methylation of placental NR3C1 proximal promoter was linked with maternal blood pressure (Monk et al., 2016). Maternal depression and greater placental DNA methylation of NR3C1, the gene encoding the GR, predicted poorer self-regulation, lower muscle tone, and more lethargy in neonates (Conradt et al., 2013; Mendonca et al., 2023; Togher et al., 2018; Wei et al., 2022), although other reports showed maternal adversity (depression, low socioeconomic status) to be associated with increased placental levels of NR3C1 mRNA(Mendonca et al., 2023; Raikkonen et al., 2014; Raikkonen et al., 2015; Togher et al., 2018). Interestingly, DNA methylation of 11β-HSD2 and NR3C1 weren’t found in this experiment, such inconsistencies could be verified in next experiment using common CpG sites and fetal parameters through early development.

Decades of research on developmental origins of health and disease indicate that this process begins in uterus (Reynolds et al., 2013). Genetic variation can significantly alter gene regulation (Paquette et al., 2014; Richardson et al., 2008), and it is possible to maternal and/or fetal genes will predispose women to experience stress and influence gene regulation in the placenta. The results of this study showed that the change of placental GC barrier caused by PS was related to the methylation of placental GC barrier related genes abcb1a and FKBP5, suggesting that epigenetics plays an important role in the process of placental GC barrier change, but the specific mechanism needs to be further explored and studied. In conclusion, PS cause DNA methylation in the intron region of placental GC barrier, make the GC barrier related proteins of offspring express abnormally, the intrauterine GC of offspring over activate and the offspring in a high corticosterone state, thus affecting their growth and development.

In conclusion, we demonstrate that prenatal stress was related to the methylation of placental GC barrier related genes P-gp (abcb1a) and FKBP5, made the GC-related genes of offspring expression abnormally, which led to placental GC barrier opening and offspring high corticosterone state, thus affecting their growth and development. This suggested that placental GC barrier gene DNA methylation is the probable early warning target of placental GC barrier opening and susceptibility of multiple diseases.

Data and code availability

The data supporting the findings of this study are available within the article and its supplemental information. The RRBS and MethylTarget sequencing datasets generated in this study can be found in the NCBI SRA database (http://www.ncbi.nlm.nih.gov/sra/) using the access number PRJNA890935 and PRJNA891021.

Author Contributions Statement

Hongya Liu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored and reviewed the article, and approved the final draft.

Rui Wang and Jiashu Zhu performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.

Ye Li performed the experiments, prepared figures and/or tables, and approved the final draft.

Jiaqi Li and Guixiang Yao analyzed the data, prepared figures and/or tables, and approved the final draft.

Shuqin Ma conceived and designed the experiments, reviewed the article and approved the final draft. Suzhen Guan conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Funding:

This work was supported by grants from the Funding the National Natural Science Foundation of China (No:81960591), the Key Project of Ningxia Natural Science Foundation (No: 2022AAC02030).

Acknowledgements

Thank you to our participants and to the Key Laboratory of Environmental Factors and Chronic Disease Control, School of Public Health and Management, Ningxia Medical University for support

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Prenatal stress increases corticosterone levels in offspring by impairing placental glucocorticoid barrier function

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Chemicals and reagents

2.2 CUMS Procedure

2.3 Placental sampling

2.4 Allocation of the offspring into groups

2.5 Measurement of selected indicators

2.5.1 Measurement of plasma corticosterone concentration

2.5.2 Reduced representation bisulfite sequencing

2.5.3 Illumina Hiseq sequencing platform (Beijing Biomarker technologieb)

2.5.4 Validation of DNA methylation status and detection of the expression levels of the identified markers

2.5.5 Quantitative real-time PCR (qRT-PCR)

2.5.6 Western Blot Analyses

2.6 Statistical analyses

3 Results

3.1 CUMS increases prenatal plasma corticosterone levels

3.2 The effects prenatal stress on offspring’s body weight and plasma corticosterone

3.3 The effects of offspring after prenatal stress during pregnancy on enzymes of DNA methylation (DNMTs)

3.4 DNA methylation status in Placenta

3.5 Effect of prenatal stress on DNA methylation of GC related genes in placenta of offspring

3.6 Effect of prenatal stress on transcription of the placental GC related genes of offspring

4 Discussion

5 Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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