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. Author manuscript; available in PMC: 2019 Nov 1.
Published in final edited form as: Hypertension. 2018 Nov;72(5):1160–1171. doi: 10.1161/HYPERTENSIONAHA.118.11650

Role of DNA de novo (de)Methylation in the Kidney in Salt-Induced Hypertension

Pengyuan Liu 1,2, Yong Liu 2, Han Liu 3, Xiaoqing Pan 2, Yingchuan Li 2,4, Kristie Usa 2, Manoj K Mishra 2, Jing Nie 3, Mingyu Liang 2,3
PMCID: PMC6314686  NIHMSID: NIHMS1504980  PMID: 30354815

Abstract

Numerous adult diseases involving tissues consisting primarily of non-dividing cells are associated with changes in DNA methylation. It suggests a pathophysiological role for de novo methylation or demethylation of DNA, which is catalyzed by DNA methyltransferase 3 (Dnmt3) and ten-eleven translocases (Tet). However, the contribution of DNA de novo (de)methylation to these diseases remains almost completely unproven. Broad changes in DNA methylation occurred within days in the renal outer medulla of Dahl SS rats fed a high-salt diet, a classic model of hypertension. Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s attenuated high salt-induced hypertension in SS rats. The high salt diet induced differential expression of 1,712 genes in the renal outer medulla. Remarkably, the differential expression of 76% of these genes were prevented by anti-Dnmt3a/Tet3 GapmeR’s. The genes differentially expressed in response to the GapmeR’s were involved in the regulation of metabolism and inflammation and were significantly enriched for genes showing differential methylation in response to the GapmeR’s. These data indicate a significant role of DNA de novo (de)methylation in the kidney in the development of hypertension in SS rats. The findings should help to shift the paradigm of DNA methylation research in diseases involving non-dividing cells from correlative analysis to functional and mechanistic studies.

Keywords: Epigenetics, DNA methylation, kidney, salt, hypertension, genomics

Introduction

DNA methylation is a major type of epigenetic modification. It mainly occurs at cytosines in cytosine-guanine (CpG) dinucleotides and can change transcriptional activities and gene expression. De novo DNA methylation is catalyzed by DNA methyltransferase 3a (Dnmt3a) or 3b (Dnmt3b) working with Dnmt3l1. The pattern of DNA methylation is maintained during DNA replication primarily by Dnmt1. Removal of DNA methylation, or DNA de-methylation, involves the ten-eleven translocase family (Tet1, Tet2 and Tet3)2.

The role of DNA methylation, including de novo methylation, is well-established in embryonic development and cell differentiation3. Extensive evidence also exists for associations between changes in DNA methylation in adult tissues and the development of diseases that primarily involve non-dividing cells, such as cardiovascular and renal diseases4-6. However, the functional role of de novo DNA methylation or de-methylation, collectively called DNA de novo (de)methylation, in these highly prevalent diseases remains nearly completely unproven7.

Studies investigating causal contribution of DNA de novo (de)methylation to disease is hampered by the lack of specific pharmacological inhibitors. Commonly used inhibitors of DNA methylation, such as decitabine, work as cytidine analogs that are incorporated into DNA during cell division and, thereby, prevent DNA methylation8. These agents, therefore, would not inhibit de novo DNA methylation that occurs in terminally differentiated, non-dividing cells that dominate organs and organ systems such as the kidney and the cardiovascular system. Genetic deletion of Dnmt3 is lethal in mice1. Mouse models with conditional deletion of Dnmt3 or Tet can be powerful tools, and they are beginning to emerge. However, some of the robust models of cardiovascular disease are only available in rats for which conditional gene deletion remains highly challenging to produce.

Hypertension is the No. 1 identifiable risk factor for death worldwide9. More than 50% of hypertensive patients have salt-sensitive forms of hypertension10. The Dahl salt-sensitive SS rat is the most commonly used model of human salt-sensitive hypertension10. The kidney plays a key role in long-term regulation of arterial blood pressure as well as the development of salt-induced hypertension in SS rats11,12. We previously reported the first maps of DNA methylation at single-base resolution in the kidneys of SS rats13. Distinct patterns of methylation profiles were observed between SS rats and a congenic salt-insensitive rat strain on a 0.4% NaCl diet or after 7 days of a 4% NaCl diet.

In the present study, we extended the previous observations to SS rats under additional experimental conditions. The results indicated substantial and rapid changes in DNA methylation in the kidneys of SS rats in response to dietary changes. We used GapmeR, a modified antisense reagent that can be effective in vivo14, to target Dnmt3a and Tet3 in the kidneys of SS rats to attenuate the changes of methylation, including possibly increases and decreases of methylation at different genomic loci. The intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s resulted in remarkable prevention of salt-induced changes in gene expression and DNA methylation and significantly attenuated salt-induced hypertension.

CONCISE METHODS

Detailed methods are available in the online-only Data Supplement. Raw sequence data and their associated processed files for RRBS and RNA-seq experiments have been made publicly available at Gene Expression Omnibus (GEO) database and can be accessed at https://www.ncbi.nlm.nih.gov/geo/ (GSE114335).

Animals

Unless otherwise indicated, rats were maintained at the Medical College of Wisconsin on the AIN-76A diet (Dyets) with free access to water as described previously15. Female rats of 7 to 11 weeks of age were used in the current study.

Reduced representation bisulfide sequencing (RRBS).

DNA methylation profile analysis was performed in renal outer medulla tissues from individual rats (7 groups, n=3-4 per group) using multiplexed RRBS as described previously13,16.

Western blot

Western blot was performed as described previously17. Coomassie blue staining of the entire membrane was used for normalization15,17-19.

Immunohistochemistry

Immunohistochemistry analysis was performed largely as described previously20.

Renal medullary interstitial infusion and measurement of arterial blood pressure in conscious, freely moving rats

Catheterization to allow renal medullary interstitial delivery of GapmeR and measurement of arterial blood pressure in conscious, freely moving rats was performed as described previously21,22. Rats were uninephrectomized to ensure the remaining kidney receiving GapmeR injection was the sole determinant of kidney function. Similar methods of renal medullary interstitial injection have been used by several laboratories to achieve medulla-targeted, intra-renal delivery of compounds23-27. We chose to target the renal medulla because this tissue region plays a particularly important physiological role in the development of salt-sensitive hypertension28.

In vivo administration of GapmeR

GapmeR’s targeting Dnmt3a and Tet3 were pooled and injected into the kidneys of conscious, freely moving rats via the renal interstitial catheter described above. The dose was 2 mg/kg body weight for each GapmeR in a total volume of 100 uL. Control rats were injected with a scrambled GapmeR at 4 mg/kg body weight in 100 uL. In some experiments, GapmeR targeting Dnmt3a only or the scrambled GapmeR were used at the dose of 2 mg/kg body weight.

RNA-seq library preparation and sequencing

RNA-seq libraries were prepared with total RNA from individual rats (3 groups, n=3-4 per group) and sequenced as we described previously13 with modifications.

Cloning-based sequencing of bisulfite-treated DNA

PCR primers were designed to produce a 377bp amplicon covering a methylation region of interest. Purified PCR products from bisulfite-treated DNA were ligated with pGEM-T Easy Vector (Promega) and transformed into DH5α competent cells. Approximately 10 colonies were picked from each sample and Sanger-sequenced to determine the methylation rate of each CpG site within the region.

Analysis of RNA-seq data

RNA-seq data were analyzed as described previously13 with modifications.

Analysis of methylation sequencing data

RRBS data were analyzed as descried previously13 with modifications, using MethylCoder29, Genomic short-read nucleotide alignment program (GSNAP)30 and metilene31. Differentially methylated regions (DMRs) were tested for significance using an additional modified Wilcoxon rank sum test. A DMR is required to contain at least five CpG sites and exhibit a methylation rate difference of greater than 5% between two tested groups.

Statistical Analysis

Differential expression of genes, DMRs, and Gene Ontology term enrichment were identified using false discover rate (FDR) < 0.05 by the Benjamini-Hochberg method. Blood pressure and protein abundance data are presented as mean ± standard error of mean (SEM). Two-sample t test was used to test the difference between two groups. Blood pressure data were tested using two-way repeated measure ANOVA followed by Holm-Sidak test.

RESULTS

Broad changes in DNA methylation in the renal outer medulla in response to a high-salt diet or diets that alter blood pressure salt-sensitivity

We reported previously that DNA methylation profiles in the renal outer medulla of SS rats changed significantly in response to 7 days of a high-salt diet and in a way that was distinct from the response of salt-insensitive SS.13BN26 rats13. Again, we chose to focus the analysis on the renal outer medulla because of its important physiological role in the development of salt-sensitive hypertension28. We began the present study by examining whether other dietary conditions that altered blood pressure or salt-sensitivity would also change DNA methylation profiles in the renal outer medulla.

We studied methylation profiles in four groups of SS rats (n=3-4). One group was obtained from a colony maintained on a purified diet, AIN-76A, containing 0.4% NaCl at the Medical College of Wisconsin (MCW) and fed the same diet since weaning until 8 weeks old when the tissues were collected for analysis. This group was designated MCWLS. Another group was obtained from a colony maintained on a non-purified diet, 5L2F, containing 0.75% NaCl at Charles River Laboratories (CRL) and fed the same diet since receipt until 8 weeks old, designated CRLLS. Additional groups of SS rats from each colony were fed an AIN-76A diet containing 4% NaCl for 14 days starting at 6 weeks of age. These groups were designated MCWHS and CRLHS.

The MCW and CRL SS colonies have essentially identical genetic backgrounds32. We have reported that mean arterial pressure increased from approximately 105 mmHg in MCWLS rats to 140 mmHg in MCWHS rats. The increase was substantially attenuated in CRL rats, only changing from approximately 102 mmHg in CRLLS to 110 mmHg in CRLHS32.

We examined genome-wide DNA methylation profiles at single-base resolution in the renal outer medulla in these four groups of SS rats using RRBS. Quality metrics of the RRBS data and bisulfite conversion rates are available in Table S1 and S2 in the online-only Data Supplement. On average, approximately 10 million reads per library were uniquely mapped to the reference genome and the bisulfite conversion rate was 99.6%. Approximately 890,000 CpG sites were covered by at least 10 reads. The complete RRBS data are available in GEO database (GSE114335).

We observed broad differences in DNA methylation profiles between the four groups of rats. Clustering of the samples based on the normalized RRBS data was consistent with the experimental grouping of the samples (Figure 1A, 1B). Clustering based on original RRBS data without normalization produced similar groupings (Figure S1 in the online-only Data Supplement). Several hundred differentially methylated regions (DMRs) were identified between the two colonies of SS rats or the same colony of rats on LS and HS diets. Approximately a quarter of the DMRs were located in transcriptional start site regions, a third in intragenic regions, and the remainder in intergenic regions (Figure S2 and Table S3 in the online-only Data Supplement). Genes associated with the DMRs in transcriptional start site or intragenic regions in each of the four comparisons were enriched for genes associated with the Gene Ontology (GO) term of sequence-specific DNA binding (FDR < 0.05) (Figure S2and Table S4 in the online-only Data Supplement). In addition, genes bearing DMRs between CRLHS and MCWHS were enriched for GO terms SH3 and PDZ domain binding and transcription activator activity, CRLLS and CRLHS for SH3 domain binding and protein binding, and MCWLS and MCWHS for protein binding.

Figure 1. A high-salt diet for 14 days changes DNA methylation profiles in the renal outer medulla of SS rats.

Figure 1.

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A. Heatmap based on DNA methylation profiles in the renal outer medulla. Each row represents a CpG site, and each column depicts methylation data from an individual rat. The color bar at the top of the graph indicates grouping of rats based on experimental conditions. B. Principal component analysis using DNA methylation profiles in the renal outer medulla. Each dot indicates a sample. Samples with the same color belong to the same group of rats. C-F. Methylation levels of genomic regions differentially methylated between CRLLS and CRLHS (C), MCWLS and MCWHS (D), CRLLS and MCWLS (E), and CRLHS and MCWHS (F). MCW, SS rats obtained from a colony maintained on a purified diet, AIN-76A, containing 0.4% NaCl at MCW; CRL, SS rats obtained from a colony maintained on a non-purified diet, 5L2F, containing 0.75% NaCl at Charles River Laboratories; LS, rats fed the same diet as the diet the respective colony was maintained on until they are 8 weeks old when the tissues were collected for analysis; HS, rats fed an AIN-76A diet containing 4% NaCl for 14 days starting at 6 weeks of age; TSS, transcriptional start site regions; DMR, differentially methylated regions. *, p<0.05; **, p<0.01.

The DMRs showed, or tended to show, higher methylation levels in rats with higher blood pressure or higher levels of blood pressure salt-sensitivity. Specifically, methylation levels of DMRs were significantly higher in CRLHS than CRLLS, regardless of where the DMRs were located relative to genes (Figure 1C). The same was true for MCWHS compared to MCWLS (Figure 1D). A tendency for higher methylation levels were observed in DMRs located in intragenic or intergenic regions in MCWLS and MCWHS compared to CRLLS and CRLHS (Figure 1E, 1F), respectively, although the differences did not reach statistical significance.

Expression of Dnmt3 and Tet isoforms in the kidneys of SS rats

The renal outer medulla consists primarily of terminally differentiated, non-dividing cells, which suggests the robust changes in DNA methylation in the renal outer medulla we observed in the present and previous studies13 were likely the consequence of de novo DNA methylation or de-methylation. Other possible contributors included changes in infiltrating cells and imperfect fidelity of methylation maintenance during DNA replication in the small number of dividing cells that may be present in the renal outer medulla. The remainder of the study focused on testing the role of de novo DNA (de)methylation.

Dnmt3 and Tet families of enzymes catalyze de novo DNA methylation and de-methylation, respectively. Each family has several members. We examined the protein abundance of Dnmt3a, Dnmt3b, Tet1, Tet2 and Tet3 in the renal medulla of SS (from the MCW colony) and salt-insensitive SS.13BN rats33. Dnmt3a appeared to be more abundantly expressed in the renal medulla than Dnmt3b (Figure 2A, 2B). In addition, Dnmt3a abundance was significantly higher in SS rats fed the 4% NaCl diet for 7 days, compared to SS.13BN rats on the same diet (Figure 2A). Dnmt3a was not differentially expressed between the rat strains on the 0.4% NaCl diet, suggesting down-regulation of Dnmt3a might be a protective mechanism that is employed by SS.13BN26 rats in response to the high-salt diet but not by SS rats.

Figure 2. Expression of Dnmt3 and Tet isoforms in the renal medulla of SS and salt-insensitive SS.13BN rats.

Figure 2.

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Western blot analysis is shown for Dnmt3a (A), Dnmt3b (B), Tet1 (C), and Tet2 (D) in the renal medulla of SS and SS.13BN (abbreviated as 13 in the bar graphs) rats maintained on a 0.4% NaCl diet (LS) or switched a 4% NaCl diet for 7 days (HS) (n=3-4). Coomassie stain of the entire membrane, part of which is shown below the Western blot, was used for normalization. *, p<0.05 vs. 13HS; two-way ANOVA followed by Holm-Sidak test.

Tet1 and Tet2 were detected but were not significantly differentially expressed (Figure 2C, 2D). Tet3 appeared to show differential expression, but the detection of Tet3 by Western blot was questionable. The band closest to the expected molecular weight of rat Tet3 (195 kDa) showed significantly higher abundance in SS rats maintained on the 0.4% NaCl diet compared to SS rats fed the 4% NaCl diet for 7 days or SS.13BN rats on either diet (n=3, p<0.05). However, several additional bands were detected around 195 kDa. It is often more difficult to find high-quality antibodies for rat proteins than for human or mouse proteins. mRNA encoding Tet3 was expressed at an abundance level of approximately 15-20 fragments per kilobase per million reads (FPKM) in the renal outer medulla according to previous RNA-seq analysis, compared to 1-2 FPKM for Tet113.

Immunohistochemistry analysis was performed in kidneys from Sprague-Dawley rats to qualitatively examine the renal distribution of Dnmt3 and Tet isoforms. Dnmt3a, Dnmt3b, Tet1 and Tet3 were broadly expressed in several tissues in the renal cortex and medulla (Figure S3 in the online-only data supplement). Particularly prominent expression was observed for Dnmt3a in distal tubules and Tet1 in the renal medulla. The latter appeared consistent with the strong band in Western blot shown in Figure 2C. Tet2 was barely detectable (Figure S3), consistent with the faint band in Western blot (Figure 2D). The quality of several of the antibodies used was supported by staining patterns in other rat tissues that were consistent with the literature (Figure S3).

We focused the subsequent interventional experiments on Dnmt3a and Tet3 because Dnmt3a was expressed at higher levels in SS rats (Figure 2A) and Tet3 was abundant and appeared to be differentially expressed.

Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s attenuates salt-induced hypertension in SS rats

We administered GapmeR’s targeting Dnmt3a and Tet3 directly into the renal medullary interstitium in SS rats. Hypertension likely involves the up-regulation of some genes and down-regulation of other genes. Similarly, hypertension might involve the increase of methylation (mediated by Dnmt3) at some genomic loci and decrease (mediated by Tet) at other loci. The goal of the combined Dnmt3a/Tet3 GapmeR experiment was to attenuate both the increase (at some loci) and decrease (at other loci) of methylation. The rats were switched to the 4% NaCl diet and tissues collected 7 days later for analysis. Dnmt3a protein abundance in the renal outer medulla was significantly decreased in rats receiving the GapmeR’s compared to rats receiving a scrambled control GapmeR (Figure 3A, 3B). Dnmt3a was knocked down by 38% (p<0.05), which was a robust degree of knockdown for in vivo administration of oligonucleotides in the kidney. Importantly, the degree of knockdown was comparable to the difference of Dnmt3a between SS and SS.13BN rats shown in Figure 2A, suggesting the degree of knockdown might be physiologically relevant. In the renal cortex, Dnmt3a was not consistently detectable by Western blot in either experimental group. Dnmt3a protein abundance in the liver (Figure S4 in the online-only data supplement) or the heart was not significantly altered by intra-renal administration of the GapmeR’s.

Figure 3. Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s attenuates salt-induced hypertension in SS rats.

Figure 3.

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A. Renal medullary interstitial administration of GapmeR’s targeting Dnmt3a and Tet3 (anti-Dnmt3a/Tet3) knocked down Dnmt3a in the outer medulla, compared with rats receiving a scrambled GapmeR. The Western blot analysis was performed at day 7 after the GapmeR administration. Part of the Coomassie blue stain used for normalization is shown under each corresponding Western blot. B. Densitometry quantification of the blot shown in panel A normalized by Coomassie stains. n=4-5, *, p<0.05 vs. rats treated with scrambled GapmeR; student t-test. C. Mean arterial blood pressure (MAP) of SS rats treated with a high-salt diet and renal medullary interstitial administration of scrambled or anti-Dnmt3a/Tet3 GapmeRs. D. Systolic blood pressure (SBP). E. Diastolic blood pressure (DBP). F. Heart rate. N=9 for scrambled GapmeR and 11 for anti-Dnmt3a/Tet3 GapmeRs. *, p<0.05 vs. scrambled GapmeR; two-way repeated measure ANOVA followed by Holm-Sidak test.

Western blot detection of Tet3 was questionable due to the presence of multiple bands, similar to that described in the preceding section. The band closest to the expected molecular weight of rat Tet3 was decreased by 12% in rats treated with GapmeR’s targeting Dnmt3a and Tet3 (n=4-5, p<0.05, student t-test). In the renal cortext, abundance of the band was decreased by 28% in the treated group (n=4-5, p<0.05, student t-test). mRNA levels of Tet3 was not changed at the 7-day time point, which, however, would not rule out changes in protein abundance at 7 days or reduction of mRNA abundance at earlier time points. Prior to the in vivo experiment, we tested five GapmeR’s targeting different parts of rat Tet3 mRNA. All five were effective in reducing Tet3 mRNA abundance in cultured rat kidney cells NRK-52E (Figure S5A in the online-only Data Supplement). GapmeR #3 was the most effective and was selected for the in vivo intra-renal administration experiment described above. The GapmeR targeting Dnmt3a was also effective in NRK-52E cells (Figure S5B in the online-only Data Supplement). Although the efficacy in cultured cells does not guarantee efficacy in knocking down protein expression in vivo, the cell culture data support the efficacy of the GapmeR targeting Tet3. Tet3 protein abundance in the liver (Figure S4) or the heart was not significantly altered by intra-renal administration of the GapmeR’s.

Together, these data indicated that the intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s resulted in robust, physiologically relevant knockdown of Dnmt3a in the kidney. Tet3 was probably, but not certainly, knocked down. The knockdown effect did not spread to the liver or the heart as described above.

Importantly, intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s significantly attenuated high salt-induced hypertension in SS rats. Mean arterial pressure was significantly lower in the treated group starting on day 4 after the administration of GapmeR’s and the initiation of the high-salt diet (Figure 3C). By day 7 on the high-salt diet, 31% of the high salt-induced increase of mean arterial pressure in the control group was prevented in the treated group. This degree of attenuation of hypertension was comparable to that observed in SS.13BN or SS.13BN26 rats compared to SS rats34,35. Significant decreases were observed for both systolic and diastolic blood pressures and appeared to be more pronounced for diastolic pressure (Figure 3D, 3E). By day 7 on the high-salt diet, 42% of the high salt-induced increase of diastolic pressure was prevented in the treated group. Heart rate was also significantly decreased (Figure 3F).

Additional groups of SS rats were treated with intra-renal administration of anti-Dnmt3a GapmeR only. The treatment tended to attenuate hypertension, but the effect appeared smaller than the effect of combined anti-Dnmt3a/Tet3 GapmeR’s and did not reach statistical significance (Figure S6 in the online-only data supplement). In SS.13BN26 rats, intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s did not have significant effects on blood pressure on the 4% salt diet (Figure S7 in the online-only data supplement).

Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s changes DNA methylation patterns in the renal outer medulla

We performed RRBS analysis in renal outer medulla tissues from the rats treated with GapmeR’s. The group of SS rats that were treated with intra-renal administration of a scrambled GapmeR (4 mg/kg) and fed the 4% NaCl diet for 7 days was designated HS_SCR. The group treated with intra-renal administration of GapmeR’s targeting Dnmt3a and Tet3 (2 mg/kg each) and fed the 4% NaCl diet for 7 days was designated HS_DT. We included in the RRBS analysis a third group of SS rats, designated “0.4% salt”, that were surgically prepared in the same way as the first two groups but used for tissue collection just prior to when we would initiate the GapmeR treatment and high salt diet. This third group served as the baseline for comparison.

Quality metrics for the RRBS data and bisulfite conversion rates are available in Table S5 and S6 in the online-only Data Supplement. On average, approximately 35 million reads per library were uniquely mapped to the reference genome and the bisulfite conversion rate was 99.9%. Approximately 2.48 million CpG sites were covered by at least 10 reads. The complete RRBS data are available in the GEO database (GSE114335).

Clustering of the samples based on the RRBS data corresponded well with the experimental grouping (Figure 4A, 4B and Table S7 in the online-only Data Supplement), suggesting reproducible shifts in methylation profiles between experimental groups. The HS_DT group and the 0.4% salt group were clustered together first. In other words, DNA methylation patterns in the HS_DT rats were more similar to the 0.4% salt group than the HS_SCR rats. Clustering based on original RRBS data without normalization produced a similar pattern of grouping (Figure S8 in the online-only Data Supplement). It suggests that broad changes in de novo DNA (de)methylation induced by a high-salt diet were substantially abolished by the administration of anti-Dnmt3a/Tet3 GapmeR’s.

Figure 4. Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s results in differential methylation in the renal outer medulla of SS rats.

Figure 4.

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A. Heatmap based on DNA methylation profiles in the renal outer medulla. Each row represents a CpG site, and each column depicts methylation data from an individual rat. The color bar at the top of the graph indicates grouping of rats based on experimental conditions. B. Principal component analysis using DNA methylation profiles in the renal outer medulla. Each dot indicates a sample. Samples with the same color belong to the same group of rats. C-E. Number and associated Gene Ontology terms for genomic regions differentially methylated in the HS_SCR group compared with the 0.4% salt group (C), the HS_DT group compared with the 0.4% salt group (D), and the HS_SCR group compared with HS_DT group (E). HS_SCR, SS rats treated with intra-renal administration of a scrambled GapmeR (4 mg/kg) and fed the 4% NaCl diet for 7 days; HS_DT, SS rats treated with intra-renal administration of GapmeR’s targeting Dnmt3a and Tet3 (2 mg/kg each) and fed the 4% NaCl diet for 7 days; 0.4% salt, SS rats surgically prepared in the same way as rats in HS_SCR and HS_DT groups but used for tissue collection just prior to when GapmeR and high-salt diet would have been given; DMR, differentially methylated regions; TSS, transcriptional start site regions; GOTERM_MF, molecular function terms in Gene Ontology; BH, Benjamini-Hochberg method.

Several DMRs were identified between each treatment group and the baseline group and between the two treatment groups. Approximately 20% of the DMRs were located in transcriptional start site regions, a quarter to a third in intragenic regions, and the remainder in intergenic regions (Figure 4C-4E). Genomic boundaries of each DMR were defined for each comparison separately, which made it difficult to examine overlaps of DMRs across comparisons. Instead, we examined overlaps of genes bearing DMRs in transcriptional start site or intragenic regions. Of 488 genes bearing transcriptional start site or intragenic DMRs between the HS_SCR group and the 0.4% salt group, 301 genes (62%) were no longer associated with DMRs between the HS_DT group and the 0.4% salt group.

Genes bearing transcriptional start site or intragenic DMRs in each of the three comparisons were enriched most prominently for genes involved in transcriptional regulation (Figure 4C-4E and Table S8 in the online-only Data Supplement), similar to the results shown in Figure S2 in the online-only Data Supplement. Specifically, several Gene Ontology (GO) terms including protein binding, calcium ion binding, PDZ domain binding, RNA polymerase II regulatory region, and transcription activator activity were significantly enriched in genes associated with DMRs in the groups treated with high salt diet and scrambled GapmeR (HS_SCR) or Dnmt3a and Tet3 Gapmer (HS_DT) in comparison with the baseline group (0.4% salt). The GO term protein binding was enriched in the comparison of HS_SCR and HS_DT.

Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s prevents a large fraction of high salt-induced differential gene expression

We next performed RNA-seq analysis in the three groups of samples used in the RRBS analysis. Quality metrics of the RNA-seq data are available in Table S9 in the online-only Data Supplement. On average, approximately 46 million reads per library were uniquely mapped to the reference genome with a mapping rate of 92.7%. An average of 79,666 transcripts corresponding to 30,316 genomic loci were detected in each library. The complete set of the RNA-seq data are available in the GEO database (GSE114335).

Clustering of the samples based on the RNA-seq data corresponded well with the experimental grouping (Figure 5A, 5B and Table S10 in the online-only Data Supplement). Similar to the methylation data shown in Figure 4A, the HS_DT group and the 0.4% salt group were clustered together first, indicating mRNA expression patterns in the HS_DT rats were more similar to the 0.4% salt group than the HS_SCR rats.

Figure 5. Intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s substantially prevents changes in gene expression induced by high salt diet.

Figure 5.

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A. Heatmap based on RNA expression profiles in the renal outer medulla. Each row represents a transcript, and each column depicts RNA abundance data from an individual rat. The color bar at the top of the graph indicates grouping of rats based on experimental conditions. B. Principal component analysis using gene expression profiles in the renal outer medulla. Each dot indicates a sample. Samples with the same color belong to the same group of rats. C. Of 1,712 genes differentially expressed between HS_SCR and 0.4% salt groups, 1,294 (or 76%) were not differentially expressed between HS_DT and 0.4% salt groups. Numbers in the venn diagram represent numbers of differentially expressed genes. D. Of the 1,294 genes shown in blue in panels B and C, 328 were significantly differentially expressed between HS_SCR and HS_DT groups. See Figure 4 for abbreviations of group names.

Remarkably, 76% of genes, or 1,294 out of 1,712 genes, differentially expressed in rats treated with high salt diet and scrambled GapmeR were no longer differentially expressed in rats treated with high salt diet and Dnmt3a/Tet3 GapmeR’s, both compared with baseline rats (Figure 5C). Of the 418 genes that were differentially expressed in both treatment groups compared to the baseline group, all except two genes were up- or down-regulated consistently in the two treatment groups (Figure S9 in the online-only Data Supplement), indicating a high degree of reproducibility of the RNA-seq analysis.

We further examined the 1,294 genes that were differentially expressed in rats treated with high salt and scrambled GapmeR but not in rats treated with high salt and Dnmt3a/Tet3 GapmeR’s. The differential expression between rats treated with scrambled GapmeR and Dnmt3a/Tet3 GapmeRs reached statistical significance for 328 of the 1,294 genes (Figure 5D). In other words, these 328 genes were significantly differentially expressed in response to the high-salt diet, which was significantly reversed by intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s. We focused the next analysis on these 328 genes.

The 328 genes were enriched for genes related to several pathways, most notably pathways involved in the regulation of cellular metabolism, inflammation and extracellular matrix (Figure S10A in the online-only Data Supplement). The 328 genes were also enriched for genes related to cardiovascular, metabolic, inflammatory and renal diseases (Figure S10B in the online-only Data Supplement). Of the 328 genes, 12 were associated with transcriptional start site or intragenic regions that were differentially methylated between the two treatment groups (Figure 6). The proportion, 12/328 or 3.7%, was significantly higher than what could be expected by chance from all DMRs and genes detected (528/30316 or 1.7%) (p=0.01, Fisher’s exact test).

Figure 6. Genes significantly differentially expressed and differentially methylated in response to intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s.

Figure 6.

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12 of the 328 differential expressed genes shown in panel D of Figure 5 were associated with genomic regions differentially methylated between rats treated with scrambled GapmeR (HS_SCR) and GapmeR’s targeting Dnmt3a and Tet3 (HS_DT). The difference between the two groups was statistically significant (FDR<0.05) in each panel (mean values were plotted). All differentially methylated regions are located in intragenic regions, except that the regions associated with Adamts9 and XLOC_022899 are located in transcriptional start site regions.

Cloning-based bisulfite sequencing was performed to further examine five CpG sites in a DMR associated with Lrp2. Methylation rates at these CpG sites calculated from cloning-based bisulfite sequencing were consistent with the RRBS analysis (Figure S11 in the online-only data supplement). The methylation level of the region was significantly decreased in rats treated with Dnmt3a/Tet3 GapmeRs compared with the scrambled GapmeR, which was consistent with the finding of the RRBS analysis (Figure S11 in the online-only data supplement).

DISCUSSION

We have established functional significance of de novo DNA (de)methylation in a classic model of cardiovascular disease in the present study, which should help to shift the paradigm of DNA methylation research in cardiovascular and other diseases involving non-dividing cells from correlative analysis to functional, mechanistic and therapeutic studies.

Numerous studies had shown associations between diseases such as cardiovascular diseases and changes in DNA methylation in tissues consisting primarily of non-dividing cells5,13,36-38. In the case of blood pressure, a study of blood cell DNA methylation in several thousand subjects identified CpG sites associated with systolic or diastolic blood pressure, which explained up to 2% of blood pressure variation beyond what could be explained by known blood pressure genetic variants39. Importantly, Mendelian randomization in that study suggested methylation at a specific CpG site might influence blood pressure while blood pressure might influence methylation at other CpG sites.

Prior to the present study, however, it was unknown whether de novo DNA (de)methylation in adult tissues could functionally contribute to the development of diseases including cardiovascular disease. The administration of anti-Dnmt3a/Tet3 GapmeR’s enabled us to demonstrate a causal, functional contribution of DNA de novo (de)methylation to the changes of DNA methylation and gene expression and the development of hypertension in the present study. Dnmt3a and Tet3 do not have any major known biochemical functions other than catalyzing de novo DNA methylation and de-methylation. In addition, the vast majority of cells in the kidney are terminally differentiated cells in which maintenance of DNA methylation during cell division is not relevant. Therefore, the phenotypic and genomic effects of anti-Dnmt3a/Tet3 GapmeR’s that we observed were likely the consequences of changing de novo DNA (de)methylation.

The GapmeR administration approach we used allowed us to achieve modest, potentially physiologically relevant knockdown and to do so at the time of exposure to disease-inducing stimuli and thus avoid lethal or developmental effects of complete loss of Dnmt3a or Tet3. Dnmt3a catalyzes the conversion of cytosine to 5-methylcytosine (5mC). Tet3 catalyzes the conversion of 5mC to 5-hydroxymethylcytosine (5hmC). RRBS does not distinguish 5mC from 5hmC. However, we have shown previously that DNA methylation in the renal outer medulla detected by RRBS was predominantly 5mC13. In addition, 5hmC is a transient modification in the process of de-methylation. Therefore, the potential value of distinguishing 5mC from 5hmC might be limited for the present study.

We administered a combination of anti-Dnmt3a and anti-Tet3 GapmeR’s. The knockdown efficiency of Dnmt3a in the renal outer medulla was clear-cut, while the efficiency for Tet3 was less certain because of questionable Western blots. Dnmt3a in the kidney can be regulated by known regulators of blood pressure such as angiotensin II40. Anti-Dnmt3a alone did not have the clear effect of combined anti-Dnmt3a/Tet3 on hypertension. It would be valuable to investigate the specific role of other isoforms of Dnmt3 and Tet individually in future studies, including isoforms that were not differentially expressed between SS and SS.13BN rats. The isoforms that are not differentially expressed could still play a permissive role in the changes of DNA methylation that are important for disease development. Future studies should also aim to investigate the functional role of methylation changes in specific cell types or at specific genomic loci. Unfortunately, conditional, cell type-specific deletion of genes remains nearly impossible to produce in rats. It is challenging, but possible, to experimentally alter methylation levels at specific genomic loci in cultured cells41,42. Doing so in animal models in vivo is much harder, although recent advances in technologies suggest it might be feasible42.

Secondary effects of the difference in blood pressure between SS rats treated with scrambled GapmeR and Dnmt3a/Tet3 GapmeR’s could contribute to some of the differential expression or methylation between the two groups. However, while about one third of salt-induced increase of blood pressure was prevented by intra-renal administration of anti-Dnmt3a/Tet3 GapmeR’s, salt-induced differential expression was prevented for three quarters of the genes. In addition, genes differentially expressed between rats treated with scrambled or Dnmt3a/Tet3 GapmeR’s were statistically enriched for genes associated with differential methylation. These findings suggest the administration of anti-Dnmt3a/Tet3 likely contributed to the changes in DNA methylation and subsequent differential gene expression.

Several of the genes showing both differential expression and differential methylation are known to be important in the regulation of blood pressure or related physiological processes. For example, Gss encodes glutathione synthase. Glutathione is a key antioxidant, and oxidative stress in the kidney is known to contribute to hypertension in SS rats28. Apln encodes apelin, which is known to regulate water and electrolyte homeostasis via mechanisms in neuroendocrine systems and the kidneys43,44. Lrp2 encodes megalin, which is highly expressed in the renal proximal tubules, including proximal straight tubules that are present in the outer medulla, and important for hormone binding and cell signaling45. Differential expression of genes that were not associated with differential methylation might represent secondary effects of changes in genes that were directly influenced by methylation changes. The differentially expressed genes in response to the administration of anti-Dnmt3a/Tet3 GapmeR’s were enriched for genes related to cellular metabolism, inflammation and extracellular matrix, which is consistent with pathways known to be altered in the kidneys of SS rats and contribute to the development of disease phenotypes of SS rats46-49.

Supplementary Material

Long In Vivo Checklist
Suppl methods tables figures
Table S10
Table S11
Table S3
Table S4
Table S7
Table S8

Perspectives.

The findings of the present study indicate DNA de novo (de)methylation in the kidney contributes to the development of hypertension in SS rats. The study provides some of the first evidence for a functional role of DNA de novo (de)methylation in adult diseases. Commonly used methylation inhibitors could cause broad loss of methylation patterns important for maintaining the identities and normal functions of cells. Specific targeting of DNA de novo (de)methylation may ameliorate disease while avoiding the toxic effect of currently available methylation inhibitors.

Novelty and Significance.

What is new?

DNA methylation is known to contribute to the development of diseases primarily involving dividing cells, such as cancer. However, whether changes in DNA methylation in tissues of non-dividing cells contribute to adult diseases, including cardiovascular diseases, remains almost completely unknown. That is despite numerous studies showing associations of changes in DNA methylation with such diseases.

What is relevant?

We used tissue-targeted knockdown of enzymes mediating DNA de novo (de)methylation to examine the functional role of DNA de novo (de)methylation in the Dahl SS rat, a model of human salt-sensitive hypertension.

Summary

DNA de novo (de)methylation in the kidney contributes significantly to salt-induced changes in gene expression and the development of salt-sensitive hypertension in SS rats.

Acknowledgments

Sources of funding

This work was supported by US National Institutes of Health (HL082798, HL121233, GM066730), American Heart Association (15SFRN23910002), and National Natural Science Foundation of China (81572256 and 81372514).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Long In Vivo Checklist
NIHMS1504980-supplement-Long_In_Vivo_Checklist.pdf (35.1KB, pdf)
Suppl methods tables figures
NIHMS1504980-supplement-Suppl_methods_tables_figures.pdf (1.1MB, pdf)
Table S10
NIHMS1504980-supplement-Table_S10.xlsx (7.7MB, xlsx)
Table S11
NIHMS1504980-supplement-Table_S11.xlsx (412.8KB, xlsx)
Table S3
NIHMS1504980-supplement-Table_S3.xlsx (1.4MB, xlsx)
Table S4
NIHMS1504980-supplement-Table_S4.xlsx (16.6KB, xlsx)
Table S7
NIHMS1504980-supplement-Table_S7.xlsx (2MB, xlsx)
Table S8
NIHMS1504980-supplement-Table_S8.xlsx (14.7KB, xlsx)

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