Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Whole transcriptome analysis of adrenal glands from prenatal glucocorticoid programmed hypertensive rodents

Abstract

Prenatal glucocorticoid exposure is associated with the development of hypertension in adults. We have previously demonstrated that antenatal dexamethosone (DEX) administration in Wistar-Kyoto dams results in offspring with increased blood pressure coupled with elevated plasma epinephrine levels. In order to elucidate the molecular mechanisms responsible for prenatal DEX-mediated programming of hypertension, a whole-transcriptome analysis was performed on DEX programmed WKY male adrenal glands using the Rat Gene 2.0 microarray. Differential gene expression (DEG) analysis of DEX-exposed offspring compared with saline-treated controls revealed 142 significant DEGs (109 upregulated and 33 downregulated genes). DEG pathway enrichment analysis demonstrated that genes involved in circadian rhythm signaling were most robustly dysregulated. RT-qPCR analysis confirmed the increased expression of circadian genes Bmal1 and Npas2, while Per2, Per3, Cry2 and Bhlhe41 were significantly downregulated. In contrast, gene expression profiling of Spontaneously Hypertensive (SHR) rats, a genetic model of hypertension, demonstrated decreased expression of Bmal1 and Npas2, while Per1, Per2, Per3, Cry1, Cry2, Bhlhe41 and Csnk1D were all upregulated compared to naïve WKY controls. Taken together, this study establishes that glucocorticoid programmed adrenals have impaired circadian signaling and that changes in adrenal circadian rhythm may be an underlying molecular mechanism responsible for the development of hypertension.

Introduction

Fetal exposure to an unfavourable in-utero environment has been strongly associated with the development of numerous adulthood diseases, a phenomenon referred to as fetal programming1. In-utero insults such as maternal undernutrition, placental dysfunction, hypoxia and fetal exposure to alcohol, nicotine and glucocorticoids (GCs) are established determinants which contribute to the programming of adult diseases in various species2,3,4,5,6,7. Emerging evidence suggests that these stressors trigger molecular reconfiguration at the cellular level as a compensatory mechanism to survive the in-utero insult4,7. This adaptation results in permanent molecular changes which increases the risk of developing disease later in life8,9. Diseases linked to fetal programming include cardiovascular disease (CVD), kidney disease, adrenal dysfunction, metabolic syndrome, insulin resistance and hypertension3,7,10,11,12,13.

We have previously shown that antenatal administration of synthetic GCs such as dexamethasone (denoted as DEX) in Wistar-Kyoto (WKY) dams results in offspring that developed increased elevated systolic, diastolic, and mean arterial pressure, along with increased plasma epinephrine levels as adults7,14. GCs are lipophilic hormones that can readily cross the placenta resulting in a stress-like in-utero environment15. The placenta expresses the 11β-dehydroxysteroid dehydrogenase 2 (11β-HSD2) enzyme which metabolizes GCs, therefore normal in-utero GC concentrations are substantially reduced in comparison to maternal levels16. A recent study demonstrated that placental expression of 11β-HSD2 is rhythmically expressed and that it is possible for rhythmic GC passage through the placental barrier17. In addition, elevated fetal GC exposure is observed in pregnancies complicated with pre-eclampsia18 or intrauterine growth retardation19 where there is reduced placental 11β-HSD2 expression. Furthermore, 11β-HSD2 is ineffective in metabolizing synthetic GCs, therefore DEX is able to pass through the transplacental passage and into the in-utero environment20. Clinically, synthetic GCs have proven to accelerate fetal lung maturation and is therefore given to pregnant women at risk of preterm birth21.

Despite evidence for GC mediated fetal programming of adult hypertension, the underlying molecular mechanisms have been largely uncharacterized. We and others have shown permanent molecular programming of genes involved in the catecholamine biosynthesis pathway in the adult adrenal glands of prenatal DEX exposed WKY rats4,7,14,22,23. Here, the programmed adrenal glands demonstrated modest upregulation of tyrosine hydroxylase, dopamine β-hydroxylase and phenylethanolamine N-methyl transferase. Indeed, the adrenal gland is part of the hypothalamic–pituitary–adrenal (HPA) axis which has been implicated with cardiovascular disorders and the development of hypertension24,25. The HPA axis contributes to the physiological response to stress, and acts on the adrenal medulla to promote the biosynthesis and secretion of the catecholamine epinephrine, which binds to adrenergic receptors throughout the body resulting in increased blood pressure. To date, a comprehensive global-scale molecular analysis of the DEX programmed adrenal gland has not been established. Identification of global gene expression alterations in the programmed adrenal glands will help elucidate the molecular mechanisms which contribute to the development of hypertension in adulthood.

In this study, a whole-transcriptome analysis was performed on DEX programmed WKY male adrenal glands using the Rat Gene 2.0 microarray (Thermo Fisher Scientific). 142 annotated significantly differentially expressed genes (DEGs) were identified in DEX exposed offspring compared with saline-treated controls (109 upregulated genes and 33 downregulated genes). Pathway enrichment and upstream regulator analyses of the DEG list demonstrated that genes involved in circadian rhythm signaling were most robustly dysregulated. RT-qPCR analysis confirmed the increased expression of circadian genes Bmal1 and Npas2, while Per2, Per3, Cry2 and Bhlhe41 were significantly downregulated in adrenals from DEX exposed animals compared to saline controls. We also determined the gene expression profile of the Spontaneously Hypertensive (SHR) rats26. Here, the SHR animals also demonstrated dysregulation of the circadian rhythm but with opposing results to the fetal programming model. Taken together, the overall data suggests that dysregulation of circadian rhythm signaling may be an underlying mechanism for the development of hypertension.

Methods

Animals, DEX injections, and tissue collection

WKY (Wistar Kyoto) and SHR (Spontaneously Hypertensive) rats were purchased from Charles River Laboratory (Montreal, QC, Canada) and housed in Laurentian University’s animal care facility. All protocols were approved by the Laurentian University Animal Care Committee in accordance with guidelines from the Canadian Council on Animal Care. Rats were exposed to a 12-h light–dark cycle, with the light phase set between 6:00 am to 6:00 pm. Food and water were available ad libitum.

WKY rats were fetal programmed with DEX as previously shown4,7. Briefly, WKY male and female rats (aged 8 weeks) were acclimatized for 2 weeks. One male rat was placed with three female rats. The females were monitored for vaginal plugs daily and housed individually once the plugs were observed. Pregnant females were administered subcutaneous injections of DEX throughout the third trimester (days 15 – 21) at 100 μg/kg/day prepared in 0.9% NaCl with 4% ethanol, or the control saline solution. The naïve rats did not receive injections. The resulting pups were weaned at 3 weeks of age, and 2–3 rats were housed per cage according to sex. In a separate cohort, 17 week old male SHR and WKY rats were purchased and acclimatized for 2 weeks without breeding or injections. At 19 weeks, male rats were anaesthetized by an intraperitoneal administration of 75 mg of ketamine (CDMV Inc) and 5 mg xylazine (Sigma) per Kg of body weight. Adrenal glands were isolated, frozen on dry ice and stored at − 80 °C until further processing. All anesthetizations and adrenal sample collection was performed between 10 to 11 am.

RNA extraction and cDNA synthesis

Total RNA was extracted from the adrenal glands using TRI Reagent (Sigma) according to manufacturer’s instructions. Briefly, the left adrenal gland was placed in a microfuge tube with 1 mL TRI reagent and one stainless steel bead, and homogenized using a Tissuelyser (Qiagen) for 2 cycles at 30 Hz for 2 min. The homogenized tissue was centrifuged at 12,000×g for 10 min at 4 °C. The supernatant was mixed with 200 µl of chloroform (Sigma) and centrifuged. The aqueous phase which contains the RNA was carefully transferred to a fresh microfuge tube, mixed well with 500 µl of isopropanol (Sigma), and centrifuged at 12,000 × g for 8 min at 4 °C. The supernatant was discarded and the pellet was resuspended in 1 ml of 70% ethanol. The tubes were then centrifuged at 7500×g for 5 min and the ethanol was discarded. The pellet containing the purified RNA was subsequently air dried and dissolved in diethylpyrocarbonate (DEPC)-treated water. The RNA samples were analyzed using NanoDrop (ND-1000 spectrophotometer) to measure absorbance ratio at 260/280 nm and 260/230 nm in order to assess RNA purity. RNA samples below absorbance ratio of 1.8 were excluded from analysis.

Genomic DNA was removed from the purified RNA samples using the DNAseI kit (Sigma) according to manufacturer’s instructions. The RNA samples were then reverse transcribed using random hexamers (Sigma), mixed dNTPs (VWR), and M-MLV reverse transcriptase (Promega) according to manufacturer’s instructions.

Primer design and reverse transcribed-quantitative polymerase chain reaction (RT-qPCR)

Forward and reverse primer pair sequences for genes of interest were selected from Primer3 (NCBI). Design criteria for primer sequences included target sequence length between 75–150 base pairs, 50–60% GC content and melting temperatures between 57–63 °C. In addition, primer pairs were selected to span exon–exon junctions to avoid detection of genomic DNA. Primers were custom ordered from Sigma and were validated by plotting critical threshold (Cq) values against a sevenfold cDNA serial dilution on a logarithmic scale. The reaction efficiency of each primer pair was calculated according to the formula E = [10(−1/slope)− 1]. Primers with reaction efficiency between 90 to 110%, and R2 value greater than 0.99 were considered validated and acceptable for analysis. In addition, optimal annealing temperature for each primer pair was identified by performing temperature gradient analysis and identifying annealing temperature which resulted in smallest Cq value. The complete list of validated primer sequences can be found in Supplementary Table #1.

RT-qPCR reactions were performed using the Quantstudio 5 qPCR instrument (ThermoFisher Scientific) in 15 μL reaction volumes as described previously27. All samples were analyzed in duplicate and normalized to three independent control housekeeping genes (Gapdh, Rpl-13 and Rpl-32). The relative mRNA transcript level of each gene was reported according to the ΔΔCq method as mRNA fold increase: 2ΔΔCq = 2Ct gene of interest – ΔCq housekeeping genes). For each gene, average 2ΔΔCq and standard error of means (SEM) for all samples were calculated.

Whole transcriptome microarray

Total RNA from 18 male adrenal samples (6 naïve, 6 saline and 6 DEX treated rats) were sent to The Centre for Applied Genomics (TCAG) Microarray Facility (The Hospital for Sick Children, ON, Canada) for whole transcriptome profiling. RNA quality was verified on the Agilent 2100 Bioanalyzer (Agilent Technologies) to ensure samples had an RNA integrity number ≥ 8.0 and A:260/280 > 1.95. RNA samples were assayed for whole transcriptome analysis at TCAG utilizing the Rat Gene 2.0 ST GeneChip (ThermoFisher Scientific). The raw microarray data was quality checked, normalized and analyzed utilizing the Transcriptome Analysis Console (TAC) Software 4.0.2.15 (Thermo Fisher) with the rat reference genome (Rnor_5.0) to generate the differential gene expression (DEG) list. DEG selection criteria was set to fold-change < − 1.5 and > 1.5, p-value < 0.05 and false discovery rate < 0.1. TAC was also used to perform exploratory grouping analysis (EGA). The following EGA parameters were utilized: variance filter of 20,000 maximally variant genes non-weighted, t-SNE dimension reduction with perplexity = 4 and affinity clustering with affinity = 0.25.

Gene ontology (GO) enrichment and pathway analysis

GO enrichment and pathway analysis was performed using iPathwayGuide (iPG; Advaita Bioinformatics). The DEGs were analyzed in the context of pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Release 90.0 + /05–29) and the GO Consortium database (2019-Apr26). The “Impact Analysis” approach was utilized to score the GO pathways and FDR correction was applied with p-value threshold set to < 0.05 as previously described28,29.

Upstream regulator analysis

iPG was used to identify the predicted upstream gene regulators. This analysis utilized the experimental DEG enrichment data in combination with iPG’s proprietary knowledge base on regulatory interaction networks. This information was used to compute the Z-score and the corresponding p-value for each upstream regulator as previously demonstrated30. Here, the predicted activation or inhibition state of the upstream regulator was provided. Upstream regulators were considered statistically significant when FDR p-value was < 0.05.

Statistics

The data for the RT-qPCR experiments is presented as mean ± SEM. Between group comparisons were performed using one-way ANOVA followed by Tukey’s post-hoc analysis. Statistical significance was identified for comparisons with p-value < 0.05. Statistical analysis for the microarray data is detailed in the TAC User Guide (assets.thermofisher.com/TFS-Assets/LSG/manuals/tac_user_manual.pdf). The statistical parameters for iPG analyses is outlined here (https://www.advaitabio.com/ipathwayguide).

Results

Whole transcriptome analysis

In order to elucidate the molecular mechanisms responsible for prenatal DEX-mediated hypertension, whole-transcriptome analysis was performed using the adrenal glands of male offspring (19-week-old) of naïve, saline or DEX exposed WKY dams. The RNA samples were analyzed using the Rat Gene 2.0 ST GeneChip microarray (ThermoFisher Scientific). This array covers over 28,000 protein coding transcripts from 23,500 Entrez genes, with a median of 22 probes per gene thereby providing excellent genome wide coverage. Whole transcriptome expression analysis of DEX exposed offspring compared with saline-treated controls revealed 190 significant DEGs (criteria: fold-change < − 1.5 and > 1.5; p-value < 0.05; false discovery rate < 0.1). 42 DEGs are currently unannotated or belong to the spliceosomal RNA family. The 142 annotated DEGs consisted of 109 upregulated genes and 35 downregulated genes (Fig. 1a), and illustrated as a volcano plot in Fig. 1b. The full list of upregulated and down-regulated DEGs based on fold change is presented in Supplementary Table 2. Importantly, the control comparison between the saline and naïve offspring resulted in no DEGs (Fig. 1a). Therefore, the naïve dataset was not considered for all further analyses.

Figure 1
figure 1

Overview of whole transcriptome microarray analysis in 19-week-old male WKY adrenals exposed to prenatal DEX relative to saline controls. (a) The Rat Gene 2.0 ST GeneChip microarray (ThermoFisher Scientific) identified 142 annotated differentially expressed genes (DEGs) in prenatal DEX versus saline-treated samples, with 109 upregulated genes and 33 downregulated genes (criteria: fold-change < − 1.5 and > 1.5; p-value < 0.05; false discovery rate < 0.1). There were no DEGs between naive and saline controls. (b) A volcano plot depicting the whole transcriptomic analysis illustrates highly dysregulated genes to the left and right sides of the plot, while genes higher on the graph indicates increased statistical significance. DEGs in DEX versus saline with p-value below 0.05 are marked in red (> 1.5 fold change) and green (< − 1.5 fold change). (c) Principal component analysis (PCA) of microarray data. PCA was performed on DEX (blue spheres) and saline (red cubes) datasets and the resulting scores for the first three principal components are presented. The three principal components accounted for 51.8% of the variance in the datasets. This analysis revealed that the DEX and saline samples form distinct groupings. (d) Exploratory grouping analysis (EGA) of whole-transcriptome datasets from DEX and saline samples. EGA was performed without pre-defining known sample attributes. Associating the sample IDs to the EGA plot demonstrates clear non-homogeneous distribution of the datasets into two distinct clusters: hypertensive DEX (blue spheres) and normotensive saline controls (red cubes). (e) Top 15 upregulated genes and (f) top 15 downregulated genes in DEX versus saline datasets presented with p-value (p) and ranked based on fold change (F.C.). (g) Validation of whole-transcriptomic microarray data via RT-qPCR analysis using 16 representative genes. Genes were randomly selected to include top up and down-regulated DEGs as well as moderate DEGs. Relative gene expression depicted as fold change is shown for both the microarray and RT-qPCR assays. Comparison of fold changes between RT-qPCR and microarray were generally similar and in the same order of magnitude.

Principal component analysis (PCA) and unbiased exploratory grouping analysis (EGA)

PCA mapping was performed on the DEX (blue spheres) and saline (red cubes) transcriptome datasets (Fig. 1c). The three principal components accounted for 51.8% of the variance in the datasets. As expected this analysis revealed that the DEX and saline samples form discrete groupings, demonstrating that the DEX and saline groups have distinct global gene expression profiles. Next the microarray samples were analyzed using TCA’s EGA module, which enables analysis of relationships between transcriptome datasets without pre-defining known sample attributes and physiological parameters. Associating the sample IDs to the EGA plot demonstrates clear distribution of the samples into two distinct spatially disparate clusters: hypertensive DEX (blue spheres) and normotensive saline controls (red cubes) (Fig. 1d). The PCA plot reveals that whole transcriptomics datasets can be harnessed to predict blood pressure physiology based solely on gene expression profiles. Taken together, the PCA and EGA demonstrate that there are distinct underlying gene expression differences between prenatal DEX and saline exposed adrenal samples.

Top dysregulated genes

The top 15 upregulated and downregulated genes in DEX versus saline dataset is presented in Fig. 1e,f respectively, ranked based on fold change. Some of the DEGs have been previously associated with the development of hypertension, including Slc9A331 (solute carrier family 9 member A3; fold change = 4.55), Hpgd32 (hydroxyprostaglandin dehydrogenase; fold change = 2.56), Pah7,30 (phenylalanine hydroxylase; fold change = 2.10), Fgf733 (fibroblast growth factor 7; fold change = 2.05) and Cyp2e134 (cytochrome p450, family 2, subfamily e1; fold change = − 2.03) 7,34,35. However, the majority of top dysregulated genes are currently not implicated in the development of hypertension. In fact, there are numerous genes for orphan olfactory receptors and uncharacterized small nucleolar RNA molecules that are present in the highly downregulated genes (Fig. 1f and Supplementary Table 2). Taken together, the DEGs discovered in this study may potentially contribute to the identification of novel molecular mechanisms underlying the fetal programming of hypertension.

RT-qPCR validation of transcriptome results

In order to confirm the transcriptomic results prior to further downstream bioinformatics analyses, selected genes from the microarray were cross-verified using RT-qPCR analysis. Genes analyzed included highly dysregulated as well as moderately expressed genes based on the transcriptome DEG list (Supplementary Table 2). Comparison of fold changes between the microarray and RT-qPCR data were equivalent (Fig. 1g), thereby providing confidence in the transcriptome data and its use in downstream bioinformatics analyses.

Gene ontology and pathway enrichment analysis

In order to understand how the DEGs affect specific biological processes, GO enrichment analysis was performed. The iPG analysis package was used to hierarchically rank the DEGs within annotated GO units identified by the GO consortia database using iPG’s proprietary “impact analysis” method28,29. FDR correction was further applied to obtain GO terms with increased statistical significance. Top 10 enriched GO terms categorized as biological processes (Fig. 2a), molecular functions (Fig. 2b) and cellular components (Fig. 2c) are presented ranked by FDR p-value (p. adjusted). The number of DEGs identified in each GO term is provided along with the total number of genes annotated within the GO database. The top biological process was circadian regulation of gene expression followed by pyrimidine nucleobase metabolic process. Other noteworthy biological ontologies identified include redox processes, fat cell differentiation, and regulation of insulin secretion. The top molecular function was oxidoreductase activity, followed by a variety of pathways involved in metabolic processes. The top cellular components primarily involved the mitochondria and the cytoplasm. Here, the lack of nuclear components in the top list demonstrates that the majority of differences in the DEX versus saline dataset is due to dysregulation of genes which express proteins that contribute to cytoplasmic and mitochondrial functions.

Figure 2
figure 2

Summary of gene ontology (GO) and global pathway analyses in DEX adrenals relative to saline controls. Top 10 enriched GO terms categorized as (a) biological processes, (b) molecular functions and (c) cellular components are presented ranked by FDR p-value (p. adjusted). The number of DEGs identified in each GO term is provided along with the total number of genes annotated within the GO database. (d) Top signaling pathways (FDR p-value < 0.05) in DEX versus saline datasets identified by pathway enrichment analysis (iPathwayGuide). The upregulated and downregulated DEGs for each pathway is listed. (e) Predicted upstream regulators (FDR p-value < 0.05) identified by iPathwayGuide upstream regulator analysis. The activation or inhibition state is indicated. Activation or inhibition indicates that the upstream regulator is activated or inhibited respectively in DEX adrenals. Taken together, the GO and functional network analyses demonstrates that genes involved in circadian rhythm signaling are robustly dysregulated in DEX adrenals relative to saline controls.

The DEGs were also subjected to iPG’s pathway enrichment analysis to identify molecular signaling families that are dysregulated in DEX relative to saline samples. Global network enrichment analysis (Fig. 2d) demonstrated that genes involved in circadian rhythm signaling (p = 0.003) were most robustly dysregulated, followed by genes involved in metabolic pathways (p = 0.007) and purine metabolism (p = 0.013). The upregulated and downregulated DEGs for each pathway are also listed. Circadian rhythm genes Bmal1 and Npas2 were upregulated while the expression of Bhlhe41, Per2, and Per3 were downregulated in DEX relative to saline. The majority of DEGs associated with metabolic pathways and purine metabolism were upregulated with limited downregulated genes (Fig. 2d). Taken together, the GO and pathway enrichment analysis demonstrates that prenatal DEX exposed adrenals have altered circadian rhythm signaling coupled with upregulation of the metabolic pathways.

Upstream master regulator analysis

To identify master transcription regulators that can potentially explain the experimental DEGs, iPG’s predicted upstream regulators analysis was performed. Rora (p = 0.007), Npas2 (p = 0.025), and Bmal1 (p = 0.045) were identified as significant (FDR p-value < 0.05) upstream regulators (Fig. 2e). This analysis also classified the upstream regulators as activated (present) or inhibited (absent). For example, inhibited refers to master regulators that are predicted to be down-regulated in DEX samples relative to saline controls, and vice versa for activated regulators. Results suggest Rora is predicted to be activated while Npas2 and Bmal1 are inhibited (Fig. 2e). Interestingly, all three significantly predicted upstream master regulators are transcription factors that are involved in circadian rhythm signalling. Taken together, multiple DEG bioinformatics analyses demonstrate that genes involved in circadian rhythm signaling are robustly dysregulated in DEX adrenals relative to saline controls.

Circadian rhythm signaling

The GO and pathway enrichment analysis, along with the upstream regulator analysis collectively establish that DEX adrenals demonstrate impaired expression of genes involved in circadian rhythm signaling. Figure 3a illustrates an overview of the main genes involved in circadian rhythm signaling. In order to fully characterize the circadian signalling pathway, gene expression for all known circadian rhythm signaling genes was analyzed by RT-qPCR (Fig. 3b). The RT-qPCR data corroborated the microarray data in showing that Bmal1 and Npas2 mRNA expression were significantly increased, while Per2, Per3, Cry2 and Bhlhe41were downregulated in DEX relative to saline adrenals (n = 6; * p < 0.05). All other circadian rhythm genes tested (Clock, Per1, Cry1, Fbxl3, Csnk1d and Csnk1e) were not significantly different in the DEX and saline controls. In particular, Clock and Npas2 are paralogues, and both proteins can dimerize with Bmal1 to form a complex that drives the transcription of Per, Cry, and Bhlhe4136. The observation that a lack of change in gene expression in Clock suggests that Bmal1-Npas2 complex is likely responsible for controlling circadian rhythm signaling in DEX exposed adrenals. In addition, the expression of Bmal1 and Npas2 should be reciprocal to Per, Cry, and Bhlhe4137. Indeed, Per2, Per3, Cry2 and Bhlhe41 are downregulated (Fig. 3b). Taken together, the circadian gene expression analysis in DEX exposed adrenals suggests underlying issues with the adrenal circadian rhythm.

Figure 3
figure 3

DEX adrenals demonstrate dysregulated expression of genes which control circadian rhythm signaling relative to saline controls. (a) Overview of the literature established circadian rhythm signaling. Bmal1 and Npas2/Clock forms a transcriptional activator complex which binds to E-Box promoter regions thereby driving the rhythmic expression of downstream genes such as Per, Cry, and Bhlhe41. Per and Cry forms a heterodimer and is phosphorylated by kinases (Csnk1). The phosphorylated Per-Cry complex drives the negative feedback loop by inhibiting further expression of Bmal1 and Npas2/Clock. Similarly, Bhlhe41 re-enters nucleus and competitively inhibits the Bmal1-Npas2/Clock complex thereby supressing expression of Per and Cry. In addition, Fbxl3 promotes polyubiquitination of Cry proteins promoting their degradation. Expression of Bmal1 and Npas2/Clock is highest during the day, while, Per and Cry expression peaks during the night. (b) Expression profiling of all known circadian rhythm signaling genes using RT-qPCR and microarray data. Bmal1 and Npas2 mRNA expression were significantly increased, while Per2, Per3, and Bhlhe41 were downregulated in DEX relative to saline adrenals (n = 6; * p < 0.05). (c) The circadian rhythm pathway diagram obtained from iPathwayGuide (https://www.kegg.jp/kegg/kegg1.html) illustrating the computed perturbation from the DEG list of DEX adrenals relative to saline controls. The pathway diagram is overlayed with the computed perturbation of each gene. The perturbation accounts both for the measured fold change for each gene and the accumulated perturbation propagated from upstream regulators. The highest negative perturbation is shown in dark blue, while the highest positive perturbation is depicted in dark red. The legend describes the values on the gradient provided as a perturbation score. Coherent cascades are shown as red arrows. These cascades are sections of the pathway where the data describing the change in the gene expression is consistent with the established circadian signaling pathway from the literature.

Figure 3c illustrates the computed perturbation in the circadian rhythm pathway for the DEX adrenals relative to saline controls based on the transcriptome DEG list. The figure reports the computed perturbation score for each gene. A negative perturbation score (dark blue) indicates that the collective gene expression in the experimental dataset will cause a downregulation in the function of the gene, and vice versa for a positive score (dark red). The reported perturbation score is based on the combination of both the measured experimental fold change and the calculated accumulated perturbation from upstream genes. Here, the accumulated perturbation was calculated by taking into account the type, function, position, and interactions of each gene on the pathway by propagating downstream the measured expression change for each DEG30. Taken together, the perturbation score indicates that the circadian gene expression in DEX adrenals leads to the activation of Bmal1-Npas2 protein complex, which in turn inhibits the function of Per, while Cry, Ror, and Rev-erba are largely unaffected. Figure 3c also illustrates coherent cascades as red arrows. These cascades are sections of the pathway where the data describing the change in the gene expression is consistent with the established circadian signaling pathway from the literature. The abundance of red arrows initiating from the Bmal1-Npas2 protein complex illustrates that the genes involved in the circadian rhythm pathway in the DEX samples show consistent directionality as established by the GO databases.

Gene expression differences in genetic versus DEX model of hypertension

We were interested in determining how the gene expression profile in the prenatal DEX induced model of hypertension compares with the SHR genetic model of hypertension. RT-qPCR analysis of selected DEGs from the DEX model with the SHR model revealed numerous underlying gene expression differences (Fig. 4). The DEX fold change data is presented relative to saline WKY controls, and the SHR fold change data is shown relative to naïve WKY controls. As mentioned earlier, the transcriptome data for both the naïve and saline controls resulted in zero DEGs, showing that the gene expression is the same for both controls (Fig. 1a). 12 DEGs and 4 genes with similar expression from the DEX transcriptome study were randomly chosen for RT-qPCR analysis. The four genes that showed no difference in the DEX study (Gpd1, Nqo1, Msl2, and Hist2h4a) were also not changed in the SHR model. In contrast, only four of the twelve DEGs (Pah, Fgf7, Nd6, and Axdnd1) chosen from the DEX transcriptome showed similar fold change in both models (Fig. 4a) while the remaining 8 genes were significantly different (Slc9a3, Plet1, Pdlim3, Sptssb, Hpgd, Gnpat, Arpp21, and Cyp2e1). Interestingly, Slc9a4 and Cyp3e1 were the top upregulated and downregulated genes respectively in the DEX model, but the SHR model showed no difference in both gene expressions (Fig. 4a). More importantly, some genes had opposing expression patterns as in Plet1 (fold change = 2.62 and − 24.05 for DEX and SHR models respectively) and Pdlim3 (fold change = 2.15 and − 23.67 for DEX and SHR models respectively). Taken together, the DEX and SHR models demonstrate distinct gene expression profiles, revealing that stress mediated DEX model of developing hypertension differs significantly to the genetic SHR model in terms of adrenal gene expression.

Figure 4
figure 4

Comparison of 19-week-old male adrenal gene expression between the DEX model and the SHR model of hypertension using RT-qPCR. Values represent fold change ± standard error of means (n = 6; * p < 0.05; red = upregulated genes; green = downregulated genes). DEX fold change is relative to saline WKY controls, whereas SHR fold change is relative to naïve WKY controls. (a) Expression of 16 genes randomly selected from the DEX transcriptome microarray. This list includes highly dysregulated as well as moderate DEGs chosen at random. The fold change data shows that the DEX and SHR model demonstrate gene expression differences. (b) Expression profiling of all known circadian rhythm signaling genes in the DEX and SHR models of hypertension. (c) Literature established 24-h rhythmic gene expression of Bmal1 (green line), Npas2 (green line), Per (orange line) and Cry (orange line) for naïve WKY animals (figure prepared by S. Tharmalingam). The illustration depicts a 12-h light–dark cycle, with the light phase set between 6:00 am to 6:00 pm. The naïve WKY rats demonstrate peak Bmal1 and Npas2 expression during the dark/light transition, while Per and Cry expression peaks 12 h later during the light/dark transition. Adrenal samples were collected during 10 to 11 am (grey shaded region).

Comparison of the circadian rhythm genes in both models also showed drastic differences (Fig. 4b). In contrast to the DEX model (Figs. 3b and 4b), the SHR model demonstrated decreased expression of Bmal1 and Npas2, while Per1, Per2, Per3, Cry1, Cry2, Bhlhe41 and Csnk1D were all upregulated compared to naïve WKY controls. Since both models demonstrate circadian system gene alterations compared to their respective controls, the overall data suggest that dysregulation of adrenal circadian rhythm may be an underlying mechanism for the development of hypertension.

Discussion

This is the first study to report a global whole transcriptome analysis of GC programmed adrenal gland. Here we have identified several novel findings. First, we applied stringent transcriptomics parameters and identified 142 significant DEGs in DEX exposed adrenals compared to saline controls. This study is the first to associate these genes in GC mediated programming of the adrenal gland. Importantly, some of these DEGs may serve as putative biomarkers for the development of hypertension. Second, we utilized EGA to unbiasedly segregate samples into normotensive (saline controls) or hypertensive (DEX exposed group) based solely on the global whole transcriptome dataset without predefining the physiological parameters. We propose that this approach can be harnessed to predict an individual’s blood pressure physiology based solely on their gene expression profiles. Third and importantly, this study established that DEX adrenals have impaired circadian rhythm signaling based on multiple DEG bioinformatics platforms including GO enrichment, network pathway analysis, and upstream regulator prediction. Finally, we show that the adrenal glands of SHR rats demonstrated distinct gene expression profiles compared to the DEX programmed adrenals. Analysis of the circadian rhythm genes showed that the SHR animals also demonstrated circadian gene dysregulations compared to naïve WKY animals.

The circadian system consists of two parts: a central clock located in the suprachiasmatic nuclei (SCN) and peripheral clocks that are present in all organ systems38,39. The central clock obtains light–dark cues (zeitgeber) from the retina and relays this information to the peripheral clocks using humoral and neuronal signals to achieve circadian entrainment40. At the molecular level, the circadian system consists of transcription-translation feedback loops that drive rhythmic expression of core clock genes and their protein products (Fig. 3a). The literature established 24-h rhythmic gene expression pattern of Bmal1, Npas2, Per and Cry for naïve WKY animals is presented in Fig. 4c36,37,39,41. The illustration depicts a 12-h light–dark cycle, with the light phase set between 6:00 am to 6:00 pm as in the case with our experimental animals. Overview of the literature indicates that the naïve/saline WKY rats demonstrate peak Bmal1 and Npas2/Clock expression (Fig. 4c; green line) during the dark/light transition, while Per and Cry demonstrates antiphasic expression (Fig. 4c; orange line) with peak levels 12 h later during the light/dark transition36,37. In this experiment, all adrenal samples were collected between 10 to 11 am. Comparison of circadian gene expression of the DEX model in the context of the 24-h rhythmic cycle shows that increased Bmal1 and Npas2, and decreased Per and Cry is expected several hours earlier during the 6 am dark/light transition37,41,42,43,44,45,46. The opposite is true for the SHR model. Here, increased Bmal1 and Npas2, and decreased Per and Cry relative to the naïve WKY animals is expected many hours later, closer to the 6 pm light/dark transition. Indeed, previous studies show that the SHR adrenals demonstrate circadian phase advance39,41, which corroborates the SHR circadian rhythm gene expression results from this study (Fig. 4b,c). Taken together, altered circadian rhythm entrainment may be an underlying molecular mechanism responsible for the development of the hypertensive phenotype observed in both the DEX and SHR models.

The Bmal1-Npas2/Clock transcriptional activator complex promotes numerous downstream effects including control of blood pressure regulation46. Indeed, various clock gene knockout models demonstrated blood pressure dysregulation. For example, Bmal1 knockout animals exhibited reduced blood pressure and lacked circadian variation throughout a 24 h cycle47. Likewise, the Clock mutant mouse model showed dampened blood pressure and heart rate rhythm48. Similarly, Per1 knockout44 lowered blood pressure while Per2 mutants42 showed decreased diastolic blood pressure coupled with elevated heart rate. Taken together, these mutant studies demonstrate that circadian genes play an integral role in the control of blood pressure. Therefore, the dysregulation of circadian genes in the DEX exposed adrenal glands reported in this study may be an underlying programming mechanism driving the development of hypertension.

At the physiological level, it has been well established that blood pressure and plasma epinephrine levels exhibit circadian rhythm oscillations40. Blood pressure and epinephrine is lowest during night and undergoes a steep increase in the morning, peaking in the late afternoon. Similarly, circulating GC levels also demonstrate rhythmic levels. GCs have a complex ultradian rhythm composed of frequent episodes of GC secretion, with peak levels in the morning. This peak GC secretion is important for coordinating the central and peripheral clocks. However, human clinical studies have shown that children exposed to antenatal GC treatment lacked a cortisol awakening response and had a flatter diurnal slope49. This correlates with studies which report that offspring exposed to maternal undernutrition during fetal development showed a loss of diurnal variation in heart rate and blood pressure50. This is clinically significant since individuals that do not display a diurnal blood pressure response have been associated with hypertension and various adverse cardiovascular outcomes46. Taken together, these studies demonstrate that desynchronization of peripheral and central clocks promote the development of hypertension.

Studies using both circadian gene expression profiling and rhythmic behavioural locomotor activity in SHR animals demonstrated that the central SCN clock was phase advanced but the output rhythm was dampened compared to WKY controls. Analysis of the peripheral clock showed tissue-specific responses. The adrenal gland, colon, and plasma exhibited circadian phase advance while the liver was unaffected compared to control rats. The circadian phase advance in the SHR adrenal glands corresponded with the advanced rhythmic levels of serum corticosterone and aldosterone41, and dampened circadian blood pressure amplitudes51. At the behavioral level, the circadian dysregulation corresponds to previously established aberrant sleep/wake cycles in the SHR animals52. Interestingly, sleep disturbances contributes to a variety of diseases associated with fetal programming including insulin resistance, metabolic disorders and hypertension53. Therefore, given the desynchrony of circadian oscillators and its effect on the physiology of the SHR model, a thorough analysis of central and peripheral circadian systems along with sleep–wake regulation in the GC fetal programming model will be valuable. Furthermore, the circadian gene expression changes underlying the DEX and SHR models of hypertension identified in thus study may be applicable to human shift workers and jet lagged individuals that present with increased blood pressure54,55.

Apart from the circadian genes, several DEGs identified in the DEX programmed adrenals have been previously associated with the development of hypertension. For example, Slc9A3 is the most highly upregulated gene in DEX adrenals relative to saline controls. This gene codes for a sodium-hydrogen (Na/H) transporter and its increased expression has been implicated in essential hypertension4,31. HPGD is another top upregulated gene and functions to inactivate prostaglandins. Prostaglandins A and E are potent vasodilators capable of lowering arterial pressure therefore increased inactivation of these molecules promote hypertension32. Likewise, Pah codes for the enzyme responsible for producing tyrosine, the precursor for the production of adrenal catecholamines which directly contributes to hypertension4,7,35. Furthermore, Pah has been recently identified as an adrenal stress sensitive gene and its expression is upregulated in adrenal glands of male rats exposed to chronic stress56. Fgf7 is another robustly upregulated DEG which has been linked to hypertension33. Repressing Fgf7 expression with mir-455-3p-1 inhibits pulmonary arterial hypertension by limiting RAS/ERK intracellular signaling. Finally, Cyp2e1 and Cxcl13 were the only downregulated genes previously associated with CVD4. Cyp2e1 is a monooxygenase and reduced expression increases oxidative stress leading to the development of cardiac right ventricular failure34. In addition, numerous loss-of-function gene promoter polymorphisms in Cyp2e1 have been identified in humans, and these mutations have been clinically associated with essential hypertension in men57. Other studies report that decreased Cyp2e1 expression is associated with obesity58. Cxcl13 is a chemokine which belongs to the inflammatory system and polymorphisms in its genotype has been associated with essential arterial hypertension59. Apart from the examples provided above, the majority of dysregulated genes presented in this study are currently not implicated in the development of hypertension. In fact, there are numerous genes for orphan olfactory receptors and uncharacterized small nucleolar/spliceosomal RNA molecules (Supplementary Table 2). Taken together, an in-depth analysis of the DEGs identified in thus study will be important for elucidating the putative hypertensive gene markers driving the fetal programming of hypertension.

Comparing select DEGs from the DEX programmed adrenals with the SHR adrenals demonstrated numerous underlying gene expression differences in both models (Fig. 4a). More importantly, we were interested in identifying dysregulated gene expression patterns that were similar in both models since hypertension is an underlying phenotype in both systems. We rationalized that this comparison will enable the identification of genes that are fundamental for the development of hypertension. The following genes showed similar expression patterns in both models: Pah, Fgf7, Sptssb, Nd6, Axdnd1. Here, Pah4,34 and Fgf733 have been previously implicated in hypertension, therefore further analysis of Sptssb, Nd6, and Axdnd1 may help elucidate whether these genes contribute to development of hypertension. In fact, a full whole transcriptome profiling of the SHR adrenals will greatly contribute to this type of analysis.

Pathway enrichment analysis showed that genes involved with metabolic pathways were significantly dysregulated in the DEX programmed adrenals. Analysis of the DEGs associated with this pathway revealed several subfamilies including purine metabolism (Pde1a, Pde6d, Pde8b, Rrmr1, Rrm2, Gmpr, Ctps1) and mitochondrial metabolism (Gpd1l, Nqo1, Dhrs3, Suox, Hk2, Gla, Galm, Tomm40, Mrpl46, and Oxsm). In addition, genes involved in lipid regulation and steroid hormone production were also dysregulated (Plpp2, Sptssb, Cyp4f4, Medag, Acot2, Ldah, Stard10). Interestingly, these pathways are predominantly driven by gene upregulation (Fig. 2d). Some studies report that cellular metabolism is under the control of circadian clocks43,45,60. Therefore perhaps the increased upregulation of these adrenal metabolic genes may be due to an underlying circadian rhythm dysregulation.

In conclusion, using unbiased whole-transcriptome analysis, we have identified several novel molecular gene expression biomarkers for GC mediated fetal programming. This study confirms that antenatal GC exposure reconfigures gene expression patterns at the cellular level thereby affecting multiple molecular pathways in adulthood. This permanent adaptation is likely the underlying mechanism which drives the development of various physiological disorders associated with fetal programming. Further studies utilizing global-scale approaches such as proteomics, metabolomics and epigenomics will be needed to fully characterize the molecular and physiological effects of fetal programming and its consequences on the development of adulthood diseases.

References

  1. Barker, D. J. In utero programming of chronic disease. Clin. Sci. (Lond.) 95, 115–128 (1998).

    CAS  Article  Google Scholar 

  2. Kwon, E. J. & Kim, Y. J. What is fetal programming? A lifetime health is under the control of in utero health. Obstet. Gynecol. Sci. 60, 506–519 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  3. Waddell, B. J., Bollen, M., Wyrwoll, C. S., Mori, T. A. & Mark, P. J. Developmental programming of adult adrenal structure and steroidogenesis: effects of fetal glucocorticoid excess and postnatal dietary omega-3 fatty acids. J. Endocrinol. 205, 171–178 (2010).

    CAS  PubMed  Article  Google Scholar 

  4. Khurana, S. et al. Fetal programming of adrenal PNMT and hypertension by glucocorticoids in WKY rats is dose and sex-dependent. PLoS ONE 14, e0221719 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. Longtine, M. S. & Nelson, D. M. Placental dysfunction and fetal programming: the importance of placental size, shape, histopathology, and molecular composition. Semin. Reprod. Med. 29, 187–196 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  6. Fajersztajn, L. & Veras, M. M. Hypoxia: from placental development to fetal programming. Birth Defects Res. 109, 1377–1385 (2017).

    CAS  PubMed  Article  Google Scholar 

  7. Nguyen, P. et al. Prenatal glucocorticoid exposure programs adrenal PNMT expression and adult hypertension. J. Endocrinol. 227, 117–127 (2015).

    CAS  PubMed  Article  Google Scholar 

  8. Tharmalingam, S., Sreetharan, S., Kulesza, A. V., Boreham, D. R. & Tai, T. C. Low-dose ionizing radiation exposure, oxidative stress and epigenetic programing of health and disease. Radiat. Res. 188, 525–538 (2017).

    ADS  CAS  PubMed  Article  Google Scholar 

  9. Sreetharan, S. et al. Ionizing radiation exposure during pregnancy: effects on postnatal development and life. Radiat. Res. 187, 647–658 (2017).

    ADS  CAS  PubMed  Article  Google Scholar 

  10. Hocher, B. Fetal programming of cardiovascular diseases in later life - mechanisms beyond maternal undernutrition. J. Physiol. 579, 287–288 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. Ojeda, N. B., Grigore, D. & Alexander, B. T. Intrauterine growth restriction: fetal programming of hypertension and kidney disease. Adv. Chronic Kidney Dis. 15, 101–106 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  12. Marciniak, A. et al. Fetal programming of the metabolic syndrome. Taiwan J. Obstet. Gynecol. 56, 133–138 (2017).

    PubMed  Article  Google Scholar 

  13. Westermeier, F., Saez, P. J., Villalobos-Labra, R., Sobrevia, L. & Farias-Jofre, M. Programming of fetal insulin resistance in pregnancies with maternal obesity by ER stress and inflammation. Biomed. Res. Int. 2014, 917672 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  14. Grandbois, J. et al. Phenylethanolamine N-methyltransferase gene expression in adrenergic neurons of spontaneously hypertensive rats. Neurosci. Lett. 635, 103–110 (2016).

    CAS  PubMed  Article  Google Scholar 

  15. Cottrell, E. C. & Seckl, J. R. Prenatal stress, glucocorticoids and the programming of adult disease. Front. Behav. Neurosci. 3, 19 (2009).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  16. Ojeda, N. B., Grigore, D. & Alexander, B. T. Role of fetal programming in the development of hypertension. Future Cardiol. 4, 163–174 (2008).

    CAS  PubMed  Article  Google Scholar 

  17. Cecmanova, V., Houdek, P., Suchmanova, K., Sladek, M. & Sumova, A. Development and entrainment of the fetal clock in the suprachiasmatic nuclei: the role of glucocorticoids. J. Biol. Rhythms 34, 307–322 (2019).

    CAS  PubMed  Article  Google Scholar 

  18. Causevic, M. & Mohaupt, M. 11beta-Hydroxysteroid dehydrogenase type 2 in pregnancy and preeclampsia. Mol. Aspects Med. 28, 220–226 (2007).

    CAS  PubMed  Article  Google Scholar 

  19. Kajantie, E. et al. Placental 11 beta-hydroxysteroid dehydrogenase-2 and fetal cortisol/cortisone shuttle in small preterm infants. J. Clin. Endocrinol. Metab. 88, 493–500 (2003).

    CAS  PubMed  Article  Google Scholar 

  20. Benediktsson, R., Lindsay, R. S., Noble, J., Seckl, J. R. & Edwards, C. R. Glucocorticoid exposure in utero: new model for adult hypertension. Lancet 341, 339–341 (1993).

    CAS  PubMed  Article  Google Scholar 

  21. Roberts, D., Brown, J., Medley, N. & Dalziel, S. R. Antenatal corticosteroids for accelerating fetal lung maturation for women at risk of preterm birth. Cochrane Database Syst. Rev. 3, CD004454 (2017).

    PubMed  Google Scholar 

  22. Wong, D. L. et al. Stress and adrenergic function: HIF1alpha, a potential regulatory switch. Cell Mol. Neurobiol. 30, 1451–1457 (2010).

    CAS  PubMed  Article  Google Scholar 

  23. Lamothe, J. et al. The role of DNMT and HDACs in the fetal programming of hypertension by glucocorticoids. Oxid. Med. Cell Longev. 2020, 17 (2020).

    Article  Google Scholar 

  24. Burford, N. G., Webster, N. A. & Cruz-Topete, D. Hypothalamic–pituitary–adrenal axis modulation of glucocorticoids in the cardiovascular system. Int. J. Mol. Sci. 18 (2017).

  25. Gold, S. M. et al. Hypertension and hypothalamo-pituitary-adrenal axis hyperactivity affect frontal lobe integrity. J. Clin. Endocrinol. Metab. 90, 3262–3267 (2005).

    CAS  PubMed  Article  Google Scholar 

  26. Doris, P. A. Genetics of hypertension: an assessment of progress in the spontaneously hypertensive rat. Physiol. Genomics 49, 601–617 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. Pirkkanen, J. et al. Transcriptomic profiling of gamma ray induced mutants from the CGL1 human hybrid cell system reveals novel insights into the mechanisms of radiation-induced carcinogenesis. Free Radic. Biol. Med. 145, 300–311 (2019).

    CAS  PubMed  Article  Google Scholar 

  28. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42–46 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. Draghici, S. et al. Onto-tools, the toolkit of the modern biologist: onto-express, onto-compare, onto-design and onto-translate. Nucleic Acids Res. 31, 3775–3781 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. Bobulescu, I. A., Di Sole, F. & Moe, O. W. Na+/H+ exchangers: physiology and link to hypertension and organ ischemia. Curr. Opin. Nephrol. Hypertens. 14, 485–494 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. Murrant, C. L. et al. Prostaglandins induce vasodilatation of the microvasculature during muscle contraction and induce vasodilatation independent of adenosine. J. Physiol. 592, 1267–1281 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. Zhou, C. et al. Mir-455-3p-1 represses FGF7 expression to inhibit pulmonary arterial hypertension through inhibiting the RAS/ERK signaling pathway. J. Mol. Cell Cardiol. 130, 23–35 (2019).

    CAS  PubMed  Article  Google Scholar 

  34. Potus, F., Hindmarch, C. C. T., Dunham-Snary, K. J., Stafford, J. & Archer, S. L. Transcriptomic signature of right ventricular failure in experimental pulmonary arterial hypertension: deep sequencing demonstrates mitochondrial, fibrotic, inflammatory and angiogenic abnormalities. Int. J. Mol. Sci. 19 (2018).

  35. Nguyen, P. et al. Regulation of the phenylethanolamine N-methyltransferase gene in the adrenal gland of the spontaneous hypertensive rat. Neurosci. Lett. 461, 280–284 (2009).

    CAS  PubMed  Article  Google Scholar 

  36. Dibner, C., Schibler, U. & Albrecht, U. The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu. Rev. Physiol. 72, 517–549 (2010).

    CAS  PubMed  Article  Google Scholar 

  37. Mavroudis, P. D., DuBois, D. C., Almon, R. R. & Jusko, W. J. Daily variation of gene expression in diverse rat tissues. PLoS ONE 13, e0197258 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  38. Richards, J. & Gumz, M. L. Advances in understanding the peripheral circadian clocks. FASEB J. 26, 3602–3613 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. Sladek, M., Polidarova, L., Novakova, M., Parkanova, D. & Sumova, A. Early chronotype and tissue-specific alterations of circadian clock function in spontaneously hypertensive rats. PLoS ONE 7, e46951 (2012).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. Astiz, M., Heyde, I. & Oster, H. Mechanisms of communication in the mammalian circadian timing system. Int. J. Mol. Sci. 20 (2019).

  41. Tanaka, S. et al. The adrenal gland circadian clock exhibits a distinct phase advance in spontaneously hypertensive rats. Hypertens. Res. 42, 165–173 (2019).

    CAS  PubMed  Article  Google Scholar 

  42. Vukolic, A. et al. Role of mutation of the circadian clock gene Per2 in cardiovascular circadian rhythms. Am. J. Physiol. Regul. Integr. Comp. Physiol. 298, R627–R634 (2010).

    CAS  PubMed  Article  Google Scholar 

  43. Karatsoreos, I. N., Bhagat, S., Bloss, E. B., Morrison, J. H. & McEwen, B. S. Disruption of circadian clocks has ramifications for metabolism, brain, and behavior. Proc. Natl. Acad. Sci. USA 108, 1657–1662 (2011).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. Stow, L. R. et al. The circadian protein period 1 contributes to blood pressure control and coordinately regulates renal sodium transport genes. Hypertension 59, 1151–1156 (2012).

    CAS  PubMed  Article  Google Scholar 

  45. Marcheva, B. et al. Circadian clocks and metabolism. Handb. Exp. Pharmacol., 127–155 (2013).

  46. Douma, L. G. & Gumz, M. L. Circadian clock-mediated regulation of blood pressure. Free Radic. Biol. Med. 119, 108–114 (2018).

    CAS  PubMed  Article  Google Scholar 

  47. Curtis, A. M. et al. Circadian variation of blood pressure and the vascular response to asynchronous stress. Proc. Natl. Acad. Sci. USA 104, 3450–3455 (2007).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. Vitaterna, M. H. et al. Mutagenesis and mapping of a mouse gene, clock, essential for circadian behavior. Science 264, 719–725 (1994).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. Edelmann, M. N., Sandman, C. A., Glynn, L. M., Wing, D. A. & Davis, E. P. Antenatal glucocorticoid treatment is associated with diurnal cortisol regulation in term-born children. Psychoneuroendocrinology 72, 106–112 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. Remacle, C., Bieswal, F., Bol, V. & Reusens, B. Developmental programming of adult obesity and cardiovascular disease in rodents by maternal nutrition imbalance. Am. J. Clin. Nutr. 94, 1846S-1852S (2011).

    CAS  PubMed  Article  Google Scholar 

  51. Lemmer, B., Mattes, A., Bohm, M. & Ganten, D. Circadian blood pressure variation in transgenic hypertensive rats. Hypertension 22, 97–101 (1993).

    CAS  PubMed  Article  Google Scholar 

  52. Carley, D. W., Trbovic, S. & Radulovacki, M. Sleep apnea in normal and REM sleep-deprived normotensive Wistar-Kyoto and spontaneously hypertensive (SHR) rats. Physiol. Behav. 59, 827–831 (1996).

    CAS  PubMed  Article  Google Scholar 

  53. Buckley, T. M. & Schatzberg, A. F. On the interactions of the hypothalamic-pituitary-adrenal (HPA) axis and sleep: normal HPA axis activity and circadian rhythm, exemplary sleep disorders. J. Clin. Endocrinol. Metab. 90, 3106–3114 (2005).

    CAS  PubMed  Article  Google Scholar 

  54. Yeom, J. H. et al. Effect of shift work on hypertension: cross sectional study. Ann. Occup. Environ. Med. 29, 11 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  55. McMahon, D. M. et al. Relationships between chronotype, social jetlag, sleep, obesity and blood pressure in healthy young adults. Chronobiol Int 36, 493–509 (2019).

    PubMed  Article  Google Scholar 

  56. Jacobson, M. L., Kim, L. A., Patro, R., Rosati, B. & McKinnon, D. Common and differential transcriptional responses to different models of traumatic stress exposure in rats. Transl. Psychiatry 8, 165 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  57. Polonikov, A. V., Ivanov, V. P. & Solodilova, M. A. CYP2E1 gene promoter polymorphism -1293G>C increases the risk of essential hypertension in men with alcohol abuse. Bull. Exp. Biol. Med. 155, 734–737 (2013).

    CAS  PubMed  Article  Google Scholar 

  58. Tomankova, V., Anzenbacher, P. & Anzenbacherova, E. Effects of obesity on liver cytochromes P450 in various animal models. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub. 161, 144–151 (2017).

    PubMed  Article  Google Scholar 

  59. Timasheva, Y. R., Nasibullin, T. R., Tuktarova, I. A., Erdman, V. V. & Mustafina, O. E. CXCL13 polymorphism is associated with essential hypertension in Tatars from Russia. Mol. Biol. Rep. 45, 1557–1564 (2018).

    CAS  PubMed  Article  Google Scholar 

  60. Eckel-Mahan, K. & Sassone-Corsi, P. Metabolism and the circadian clock converge. Physiol. Rev. 93, 107–135 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was funded by (1) Canadian Institutes of Health Research, (2) Natural Sciences and Engineering Research Council of Canada and (3) NOSMFA Research Development Fund. ST was supported by funding from MITACS postdoctoral fellowship and Bruce Power (2016–2018).

Author information

Authors and Affiliations

Authors

Contributions

S.T. and S.K. contributed to the overall design, experimentation and writing of the manuscript. A.M. performed the RT-qPCR experiments. J.L. participated in the animal experiments. T.C.T. contributed to project conceptualization, project management, training, and writing of the manuscript.

Corresponding author

Correspondence to T. C. Tai.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tharmalingam, S., Khurana, S., Murray, A. et al. Whole transcriptome analysis of adrenal glands from prenatal glucocorticoid programmed hypertensive rodents. Sci Rep 10, 18755 (2020). https://doi.org/10.1038/s41598-020-75652-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-020-75652-y

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing