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Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease

Yuting Guan, Hongbo Liu , Ziyuan Ma , Szu-Yuan Li, Jihwan Park , Xin Sheng, and Katalin Susztak

Department of Medicine, Renal Electrolyte and Hypertension Division, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

ABSTRACT Background Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA meth- yltransferase 3a and 3b are the enzymes capable of establishing de novo methylation. Methods We generated mice with genetic deletion of Dnmt3a and Dnmt3b in nephron progenitor cells (Six2CreDnmt3a/3b) and kidney tubule cells (KspCreDnmt3a/3b). We characterized KspCreDnmt3a/3b mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. Results KspCreDnmt3a/3b mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid–induced fibrosis. Whole-genome bisulfite sequencing indicated that Dnmt3a and Dnmt3b play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the de- veloping kidney but are decommissioned in the mature kidney. Loss of Dnmt3a and Dnmt3b resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by Dnmt3a and Dnmt3b were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with Dnmt3a and Dnmt3b deletion. Conclusions Our results indicate a potential locus-specific convergence of genetic, epigenetic, and de- velopmental elements in kidney disease development.

JASN 31: ccc–ccc, 2020. doi: https://doi.org/10.1681/ASN.2019080797

Cytosine methylation is erased and reestablished to 50% of the genome.4 Recent studies indicated that between generations. DNA methylation is removed Dnmt1 deletion in Six2-positive nephron progenitors fromthezygotebytheblastocyststageandrein- stated during embryonic development.1 De novo meth- Dnmt3a Dnmt3b yltransferases 3a ( )and3b( )playkey Received August 10, 2019. Accepted December 24, 2019. roles in establishing new methylation patterns.2 Y.G. and H.L. contributed equally to this work. Dnmt3a-deficient animals die several weeks after birth and Dnmt3b-deficient animals die in utero, indicating Published online ahead of print. Publication date available at the essential roles of Dnmt3a-andDnmt3b-mediated www.jasn.org. de novo methylation in development. Correspondence: Dr. Katalin Susztak, Renal Electrolyte and Cytosine methylation has several important Hypertension Division, Department of Medicine, Department of Genetics, Perelman School of Medicine, University of Pennsyl- functions. Most cytosines in the genome are meth- vania, 12-123 Smilow Translational Research Center, 3400 Civic ylated for efficient silencing of transposable ele- Center Boulevard, Philadelphia, PA 19104. Email: ksusztak@ ments.3 Transposable elements are the footprint pennmedicine.upenn.edu of ancient integrated retroviruses, making up close Copyright © 2020 by the American Society of Nephrology

JASN 31: ccc–ccc,2020 ISSN : 1046-6673/3104-ccc 1 BASIC RESEARCH www.jasn.org resulted in a release of transposable-element silencing, endoge- Significance Statement nous retroviral expression, cytokine release, and a downstream severe kidney developmental defect.5 Cytosine methylation plays a key role in determining cell fate and Cytosine methylation is believed to be a key regulator of response to stimuli. Using mice with kidney-specific deletion of de novo gene expression.6 Gene regulatory regions, such as promoters genes encoding DNA methyltransferases Dnmt3a and – Dnmt3b, the authors showed that these genes are responsible for and enhancers, contain cytosine-guanine (CpG) rich regions methylation of gene regulatory regions that act as enhancers during (islands). In general, unmethylated promoters are permissive kidney development but are then decommissioned in adult mice. to transcription-factor binding and are associated with active Although the knock-out mice displayed no obvious kidney abnor- gene expression. Methylated promoters exclude transcription malities at baseline, they showed resistance to induced AKI. The factors, therefore they are associated with gene repression. authors also discovered that human kidney disease risk loci were enriched on fetal regulatory regions (enhancers) that were de- Methylation of promoter and enhancer regions plays a key commissioned by Dnmt3a/3b and no longer active in the adult role in stabilizing linage decisions and restricting lineage fates. kidney. These findings suggest that adult kidney diseases could In addition to promoters, enhancers are critical for establish- have a developmental origin and that genetic and epigenetic (such ing cell type–specific gene regulation and gene expression. as Dnmt3a/3b) factors could converge on the same genetic regions Enhancers are enriched for cell type–specifictranscription resulting in kidney disease development. factor binding sites to ensure cell-specificgeneregulation. Cell type–specific genes often have multiple enhancers that glycemic control on diabetic kidney disease development loop around and join promoters to establish a cell type—specific can be observed even decades after improved metabolic gene expression pattern. Six2 is a critical transcription factor in control.19,20 It also remains unclear how environmental kidney development. Six2-positive progenitors can undergo a and genetic factors interact and lead to kidney disease symmetric and asymmetric division to renew or to commit development. and differentiate into specialized nephron epithelium seg- To understand the role of de novo methylation in kidney cell ments.7,8 The role of cytosine methylation in this process is differentiation, we generated mice with genetic deletion of poorly understood. Dnmt3a and Dnmt3b in nephron progenitor cells (NPCs) Cre Cre Cre Kidney disease is a complex gene environmental disease, and tubule cells, using Six2 and Ksp (Six2 Dnmt3a/3b Cre affecting 800-million people worldwide. Genome-wide asso- and Ksp Dnmt3a/3b), respectively. Whole-genome bisulfite ciation analyses have been conducted to understand the her- sequencing (WGBS) and reduced representation bisulfite se- itability of kidney function, which uncovered close to 300 loci quencing (RRBS) identified significant changes in the meth- associated with disease risk.9–11 Each nucleotide variation only ylome of kidney tubule cells. We showed that Dnmt3a and increases disease risk by a minuscule amount, however, they Dnmt3b play important roles in de novo methylation of fetal should explain close to 50% of disease risk in aggregate. It has enhancers that were initially bound by Six2. The decline in been proposed that human disease-associated genetic variants Six2 expression during development was associated with a loss are enriched on cell type–specificenhancerregions.12 of H3K27ac and an increase in methylation. These fetal en- Nucleotide-sequence changes at enhancer regions could alter hancers decommissioned by Dnmt3a and Dnmt3b were en- transcription-factor binding, leading to quantitative differ- riched for kidney disease risk loci. Diseased kidney samples ences in gene expression contributing to disease development. showed a methylation pattern that was similar to the Dnmt3a/ Upon analyzing kidney disease risk loci, we found that only 3b knockout animals. Overall, our data suggest that changes 20%–30% of identified loci are located in regions annotated as broughtonbyDnmt3a/3b might be important for human enhancers in adult kidney samples. The underlying mecha- kidney disease. nism explaining the disease development that is associated with regions with no detectable regulatory function in the adult human kidney remains unknown. These regions might METHODS be specific for rare kidney cell types or a disease or develop- mental stage that is not captured by bulk analysis of adult Animal Strains human kidney tissue samples. Mice were raised and maintained in a barrier facility. Exper- Environmental and nutritional alterations play equally im- iments were reviewed and approved by the Institutional portant roles in kidney disease development.13–15 Intrauterine Animal Care and Use Committee of the University of Penn- nutrient availability is known to be an important determinant sylvania and were performed in accordance with the institu- of hypertension and kidney disease development, so called tional guidelines. For folic acid–induced nephropathy mouse “prenatal programming.”16,17 Because epigenome-editing en- models, 8-week-old male mice were injected with folic acid zymes need substrates from the intermediate metabolism, it (250 mg/kg, dissolved in 300 mM sodium bicarbonate) intra- has been proposed that the epigenome might play a key role in peritoneally and euthanized on day 7. For the cisplatin- prenatal programming. Nutrient availability, such as the pres- induced injury model, 8-week-old male mice were injected ence of diabetes, remains the most significant risk factor for with cisplatin (25 mg/kg) intraperitoneally and euthanized kidney disease development.18 Indeed, the effect of poor on day 3.

2 JASN JASN 31: ccc–ccc,2020 www.jasn.org BASIC RESEARCH

Real-Time RT-PCR WGBS RNA was isolated from mouse kidney using Trizol (Invitro- Genomic DNA was isolated from Cdh16-positive cells using gen) and was reverse transcribed using the cDNA Archival Kit the MagAttract HMW DNA Kit (Qiagen) according to the (Life Technologies). Real-time RT-PCR was performed using manufacturer’s instruction. The concentration of DNA was the SYBR Green Master Mix (Applied Biosystems). Primer measured using the Quant-iT PicoGreen dsDNA Assay kit pair sequences are shown in (Supplemental Table 1). (Life Technologies) following the manufacturer’sinstruction. DNA quality was checked on agarose gel. After trimming BUN and Creatinine Level adapter and low-quality reads by Trim Galore, Bismark was Serum creatinine was measured using Creatinine Enzymatic used for alignment to the mouse genome (mm10), dedupli- and Creatinine Standard (Pointe Scientific). Serum BUN was cation, and quantification of methylation level for each CpG measured using Infinity Urea Liquid Stable Reagent (Thermo site. SMART version 2.2.824 was used to perform de novo ge- Scientific). Both measurements were performed according to nome segmentation with default thresholds. The fragments the manufacturers’ instructions. with at least five CpG sites and methylation difference .20% were identified as DMRs. Staining Kidneys were harvested from mice, rinsed in PBS, fixed in 10% RNA Sequencing formalin, and embedded in paraffin. Tissue sections were Total RNA from whole kidneys were isolated using the RNeasy stained with hematoxylin and eosin. Mini Kit (Qiagen). RNA quantity and quality was analyzed on the 2100 Bioanalyzer (Agilent) using the RNA 6000 Pico Kit Isolation of CDH161 Cells (Agilent). Sequencing reads were aligned to the mouse ge- Cre Kidneys were harvested from control and Ksp Dnmt3a/3b nome using STAR version 2.2.125 and gene expression was double knockout mice and minced using a razor blade. About quantified using RSEM version 1.3.1.26 Comprehensive gene 0.25 g tissue was digested in 1.17 ml RPMI plus 50 mlEnzyme annotation (gencode.vM18.annotation.gtf) was obtained D, 25 mlEnzymeR,and6.75ml Enzyme A from the Multi from GENCODE.27 Differentially expressed genes (DEGs) Tissue Dissociation Kit 1 (Miltenyi Biotec) and incubated for were identified using edgeR version 3.24.3,28 with the thresh- 10 minutes at 37°C. Kidney was then dissociated using 21- olds of false discovery rate ,0.001 and log2 fold change .1. gauge and 26.5-gauge needles and incubated for 10 minutes Functional enrichment of DEGs were performed using DAVID at 37°C. The dissociation step was repeated twice. To neutral- database version 6.829 and the modPhEA database.30 ize the enzymes, 10% serum was added. Cells were then fil- tered through the 70-mm nylon mesh to isolate single cells and Functional Annotation of DMRs they were then centrifuged at 1000 rpm for 5 minutes. The We downloaded mouse kidney reference epigenomes, includ- pellet was treated with red blood cell lysis buffer and washed ing WGBS and histone modifications (embryonic day 14.5 with PBS. The cell suspension was incubated with CDH16 [E14.5], E15.5, postnatal day 0 [P0], and 8 weeks old) from antibody (Santa Cruz). CDH16-positive cells were magneti- the ENCODE website (Y. He, M. Harihran, D. U. Gorkin, D. E. cally isolated using anti-mouse IgG microbeads (Miltenyi Dickel, C. Luo, R. G. Castanon, et al., unpublished observa- Biotec) following the manufacturer’s instruction. tions;D.U.Gorkin,I.Barozzi,Y.Zhang,A.Y.Lee,B.Li, Y. Zhao, et al., unpublished observations). To define regula- RRBS tory elements, mouse kidney chromatin states (E14.5, E15.5, Genomic DNA from whole kidney was isolated using the E16.5, and P0) were downloaded from http://enhancer.sdsc. DNeasy Kit (Qiagen). Libraries were generated from the iso- edu/enhancer_export/ENCODE/chromHMM/replicated/. lated DNA using the Premium Reduced Representation Bisul- These chromatin states were estimated by ChromHMM fite Sequencing Kit (Diagenode) following the manufacturer’s (D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee, B. Li, Y. Zhao, instruction. The 2100 Bioanalyzer (Agilent) and High Sensi- et al., unpublished observations) using a 15-state model. tivity DNA Kit (Agilent) was used for quality check. After ChromHMM uses a combinatorial pattern of histone modifi- trimming adapter and low-quality reads using Trim Galore cations (H3K4me1, H3K4me2, H3K4me3, H3K27ac, version 0.5.0 (https://github.com/FelixKrueger/TrimGalore) H3K27me3, H3K9ac, H3K9me3, and H3K36me3). The 15- with the option “–rrbs,” Bismark version 0.19.121 was applied state model was further simplified into four states including to align reads to the mouse genome (mm10). MethylKit ver- promoter, enhancer, transcription, and other. Chromatin sion 1.8.122 was used to quantify the methylation level of CpG states from multiple data sets were merged. BEDTools version sites covered by at least five reads and to calculate the meth- 2.27.031 was used to intersect chromatin states and DMRs. ylation difference between Dnmt3a/3b and control samples. Adult kidney chromatin states from 8-week-old mouse CpG sites with methylation difference .20% and q value were downloaded from https://github.com/gireeshkbogu/ ,0.01 were analyzed in edmr version 0.6.4.123 to identify dif- chromatin_states_chromHMM_mm9 and lifted over to ferentially methylation regions (DMRs) with at least three mm10 using the LiftOver tool (https://genome.ucsc.edu/cgi- CpG sites and methylation difference .20%. bin/hgLiftOver). These chromatin states were estimated by

JASN 31: ccc–ccc,2020 Role of Dnmt3a/Dnmt3b in Kidney 3 BASIC RESEARCH www.jasn.org

A BC 0.04 P=0.0008 0.5 0.03 0.4 0.3 0.02 0.2 0.01 P=0.082 0.1 Relative expression 0.00 Creatinine (mg/dL) 0.0

Dnmt3a Dnmt3b Cre Con p Control Ks Cre Ksp Dnmt3a/3b Dnmt3a/3b

DECon Ksp Cre Dnmt3a/3b

Proximal tubule Loop of Henle Distal tubule Collecting duct Slc34a1 Slc12a1 Slc12a3 Aqp2 20 12 6 4

20x 10 15 3 8 4 10 6 2 4 2 5 1 ** * 2 ***

Relative expression 0 0 0 0

60x 3d 3d 3d 3d 10d 21d 56d 10d 21d 56d 10d 21d 56d 10d 21d 56d Control Ksp Cre Dnmt3a/3b

F G Kim1 Lcn2 Cd68 Ccl2 P=0.065 P=0.005 P=0.037 P=0.027 P=0.012 P=0.012 P<0.0001 P=0.0002 200 P=0.020 8000 80 20 P<0.0001 2500 2000 6000 1500 60 15 150 1000 4000 500 2000 40 10 100 50 500 40 400 30 300 BUN(mg/dL) 50 20 5 20 200 100

Relative expression 10 0 0 0 0 0

Con+Veh Ksp Cre Dnmt3a/3b+Veh Con+Cisplatin Ksp Cre Dnmt3a/3b+Cisplatin H Slc34a1 Slc12a1 Slc12a3 P=0.001 4 1.5 P<0.0001 1.5 3 1.0 1.0 2 0.5 0.5 1 Relative expression Relative expression Relative expression 0.0 0.0 0

Con+Veh Ksp Cre Dnmt3a/3b+Veh Con+Cisplatin Ksp Cre Dnmt3a/3b+Cisplatin

Figure 1. Phenotypic characterization of KspCreDnmt3a/Dnmt3b mice. (A) Breeding scheme for generating KspCreDnmt3a/3b double knockout mice (KspCreDKO). (B) Transcript levels of Dnmt3a and Dnmt3b in kidneys of 3-day-old control and KspCreDnmt3a/3b mice. Data are represented as mean6SEM; P value was calculated by two-tailed t test. (C) Serum creatinine measurement in 3-week-old control and KspCreDnmt3a/3b mice. Data are represented as mean6SEM. (D) Representative images of haemotoxylin and eo- sin–stained kidney sections of control and KspCreDnmt3a/3b mice. Scale bar: upper 20mm; bottom: 10mm. (E) Relative mRNA levels of kidney segment markers Slc34a1, Slc12a1, Slc12a3,andAqp2 in control and KspCreDnmt3a/3b mice on day 3, 10, 21 and 56. Data are represented as mean6SEM. *P,0.05, **P,0.01, ***P,0.001 by two-way ANOVA with post hoc Tukey test. (F) Serum BUN levels in control and KspCreDnmt3a/3b mice with or without cisplatin treatment. Data are represented as mean6SEM; P value was calculated by

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2 ChromHMM using the 15-state model using the combinato- (P,5310 8) SNPs from different studies were combined rial patterns of chromatin immunoprecipitation followed by (26,637). The human SNP coordinates were converted to high-throughput sequencing (ChIP-seq) profiles (H3K4me1, mouse genome (mm10) using LiftOver from the UCSC Ge- H3K4me3, H3K36me3, H3K27me3, H3K27ac, CTCF, and nome Browser,39 resulting in a final set of 7923 eGFR SNPs. RNA polymerase II) obtained from the mouse ENCODE proj- ect.32 Similarly, the 15-state model was simplified into four Quantification and Statistical Analysis states such as promoter, enhancer, transcription, and other. Statistical analyses were performed using R or GraphPad Chromatin accessibility of DMRs in kidneys of 8-week-old Prism software (GraphPad Software, La Jolla, CA). Two- male C57BL/6J mice were quantified by deepTools version tailed t test or Wilcoxon signed rank sum test was used to 3.1.233 using bigwig files obtained from the Mouse sci- compare two groups. One-way ANOVA was used to compare ATAC-seq Atlas.34 Transcription factor motif enrichment multiple groups. Spearman rank correlation was used to de- analysis of DMRs was performed using findMotifsGenome.pl termine the correlation. When needed, multiple testing cor- of HOMER version 4.10.3.35 GREAT version 3.0.036 was used rection was performed using the false discovery rate. to predict functions of cis-regulatory regions. Data Availability Six2 Binding Sites in NPCs All sequencing data (WGBS, RRBS, and RNA sequencing) Six2 binding sites (ChIP-seq peaks) in NPCs were downloaded have been deposited in the National Center for Biotechnology from GUDMAP database (RID:Q-Y4CY).37 Six2 peaks iden- Information’s Gene Expression Omnibus (GEO) and are ac- tified in at least two of the three replicates were used for further cessible through GEO accession number GSE134267. analysis. To build a control set, shuffled regions, matching in size and number, were generated using BEDTools shuffle. RESULTS Topologically Associating, Domain-Constrained Map of Enhancer DMR-DEG Associations Deletion of de Novo Methyltransferases Dnmt3a and We obtained a topologically associating domain-constrained Dnmt3b in Renal Progenitors map of enhancer-promoter associations from a reference To understand the role of de novo methyltransferases in kidney (D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee, B. Li, Y. Zhao, development and maturation, we generated double knockout f/f et al., unpublished observations). BEDTools was used for in- transgenic mice by crossing the Dnmt3a mice and the f/f Cre Cre tersect analysis. The Spearman correlation test was performed Dnmt3b mice with Ksp mice (Ksp Dnmt3a/3b or Cre Cre to examine the relationship between enhancer DMR (eDMR) Ksp DKO) (Figure 1A). Ksp mice express Cre recombi- methylation and DEG expression. nase under the control of the mouse cadherin 16. Chd16 or Ksp-cadherin is expressed from E11.5 in the developing kid- Enrichment of Genome-Wide Association Study ney (Supplemental Figure 1A). Its expression increases as the Single-Nucleotide Polymorphisms in Developmental kidney matures (Supplemental Figure 1B). Cell type–specific DMRs and Dnmt3a and Dnmt3b Double Knockout expression and open chromatin data indicated that it is ex- DMRs pressed in distal tubules, collecting duct, loop of Henle, and Genome-wide association study (GWAS) single-nucleotide proximal tubules in adult kidney (Supplemental Figure 1, C Cre polymorphisms (SNPs) were obtained from the GWAS catalog and D). Ksp Dnmt3a/3b mice were born at the expected (gwas_catalog_v1.0-associations_e96_r2019–06–20.tsv).38 Mendelian ratio. The genetic deletion was confirmed by quan- After filtering out SNPs for missing coordinates and signifi- titative RT-PCR in kidneys of 3-day-old mice (Figure 1B). 2 cance (P,5310 8), 79,744 SNPs were used for follow-up Transcript levels of Dnmt3a and Dnmt3b were lower in Cre analysis. The University of California Santa Cruz (UCSC) Ge- Ksp Dnmt3a/3b mice (Figure 1B); however, as reported ear- nome Browser LiftOver function was used to lift over the lier, the expression of Dnmt3b was around the detection limit mouse coordinates into human coordinates,39 resulting in a at birth (Figure 1B). Cre final set of 36,045 GWAS SNPs. The hypergeometric test was Ksp Dnmt3a/3b mice showed no obvious renal pheno- used to determine the significance of disease trait and enrich- typic alterations at baseline. Serum creatinine level of Cre ment of DMRs. Ksp Dnmt3a/3b was comparable to littermate controls SNPs showing a significant association with eGFR were (Figure 1C). Kidney structural analysis showed no observable downloaded from recent publications.9,40,41 The significant abnormalities (Figure 1D). To further understand the role of

one-way ANOVA. (G) Relative mRNA level of AKI markers (Kim1 and Lcn2) and cytokines (Cd68 and Ccl2). Data are represented as mean6SEM; P value was calculated by one-way ANOVA. (H) Relative mRNA level of kidney segment markers such as Slc34a1, Slc12a1, and Slc12a3 in control and Six2CreDnmt3a/3b mice with or without cisplatin treatment. Data are represented as mean6SEM; P value was calculated by one-way ANOVA. Con, control; Veh, vehicle.

JASN 31: ccc–ccc,2020 Role of Dnmt3a/Dnmt3b in Kidney 5 BASIC RESEARCH www.jasn.org

A Control Control B 7,184 Six2 Cre DKO DMRs 1,457 KspCre DKO DMRs 5,010 Developmental DMRs including 20,953 DMCs including 3,045 DMCs including 13,194 DMCs

100 Loss Gain Loss Gain Loss Gain 6,777 407 1,039 418 1,546 3,464 75 94.3% 5.7% 71.3% 28.7% 30.9% 69.1% KspCre DKO Six2 Cre DKO

50 3-week-old 25

Kidney -log10 (q value)

0

Reduced representation -1.0 -0.5 0 0.5 1.0 -1.0 -0.5 0 0.5 1.0 -1.0 -0.5 0 0.5 1.0 bisulfite sequencing Methylation difference Methylation difference Methylation difference Cre Cre (RRBS) (Six2 DKO – Control) (Ksp DKO – Control) (P21 – P0)

C E Fetal

Cre Cre Dnmt3a/b Dnmt3a/b

Cre Control Ksp Six2

Cre Cre Cre (Week 3) DKO DKO 2 E14.5 E15.5 E16.5 P0 100% Six2 Hypo-DMRs Ksp Hypo-DMRs Fetal Control (WeekKsp 3)Six 10% Both # 1.0 Hypo-DMRs 29% 80% n=196 ** 36% Developmental DMC 0.5 60% No CpGs detected Cre 32% Ksp Only 6% No DMCs Hypo-DMRs Hypo DMCs n=843 40% 0.0 12% Hyper DMCs 48% & Fraction of Hypo-DMRs 20% * 1.0 27% *

0% 0.5 Mean methylation D Loss of methylation in Dnmt3a/3b Cre Six2 Only 0.0 Hypo-DMRs n=6,581 * * 1.0 * KspCre Six2 Cre Only Only 0.5 n=843 n=6,581 DNA methylation Both 0.0 n=196 0.0 0.25 0.5 0.75 1.0

Figure 2. Dnmt3a and Dnmt3b play important roles in establishing methylation patterns during kidney development. (A) Schematic representation of RRBS on whole-kidney lysates of KspCreDnmt3a/3b, SixCreDnmt3a/3b and littermate controls. (B) Volcano plot showing methylation changes in (DMR or differentially methylated cytosines [DMCs]) in SixCreDnmt3a/3b (left), KspCreDnmt3a/3b (middle), and development (right). The x axis shows mean methylation differences and the y axis shows statistical significance (as Cre Cre 2log10[q value]). (C) The overlap between DMRs identified in Six Dnmt3a/3b, Ksp Dnmt3a/3b andduringkidneydevelop- ment (P0–P21). (D) Overlap of hypo-DMRs identified in KspCreDnmt3a/3b and SixCreDnmt3a/3b. (E) Methylation changes during mouse kidney development (E14.4, E15.5, E16.5, and P0), followed by methylations in KspCreDnmt3a/3b, SixCreDnmt3a/3b and littermate controls (left). Mean methylation levels from zero (blue) to one (red); mean methylation levels of DMRs in fetal, littermate controls, KspCreDnmt3a/3b and SixCreDnmt3a/3b kidneys (right). Mean methylation difference between control kidney and fetal, 2 KspCreDnmt3a/3b and SixCreDnmt3a/3b kidney were compared by Wilcoxon signed rank sum test. #P53.8310 6, &P50.84, 2 *P value ,2.2310 16.DKO,doubleknockout.

Dnmt3a and Dnmt3b in renal development, we quantified the day 3, 10, 21, and 56. Gene expression levels showed minor expression of renal segment–specific markers (Figure 1E). alterations in the developing and maturing kidneys, however Cre Slc34a1 (proximal tubule), Slc12a1 (loop of Henle), Slc12a3 they were overall similar between adult Ksp Dnmt3a/3b mice (distal tubule), and Aqp2 (collecting duct) were quantified on and littermate controls (Figure 1E).

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A B 980,525 fragments C DMRs after Dnmt3ab DKO (>=5 CpGs) by SMART (n=17,578) 10 1.0 Cdh16 MACS Hypo- Hyper- 4,302 Control 3-week-old antibody column DMRs DMRs 8 0.8 Whole 13,276 4,302

genome cells bisulfite 6 + 0.6 sequencing (WGBS) 4 0.4 Ksp Cre Kidney Kidney cell Cdh16+ Log2 number of

2 CpG methylation CpGs in segment Dnmt3ab suspension cell DKO Cdh16 0.2 sorting DKO and antibody 0 13,276 incubation 0.0 -1.0 -0.5 0.0 0.5 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Methylation difference in CpG methylation Cdh16+ cells (DKO - Control) Control Cdh16+ cells

DE GAll CpGs Fetal Adult Fetal Promoter Kidney Kidney Adult Promoter states states Fetal Enhancer Mouse phenotype Adult Enhancer 100% 1.0 Abn. kidney cortex morphology Chromatin states Abn. renal corpuscle morphology Others 80% 0.8 Abn. renal glomerulus morphology Transcript Abn. urinary system development Enhancer Hypo-DMRs Abn. kidney development 60% Promoter 0.6

Abn. oogenesis

Abn. nerve conduction 40% 0.4

Enhanced behavioral response to alcohol

38% Global CpG methylation Decreased nerve conduction velocity 20% 0.2 Fraction of DMRs/non-DMRs Hyper-DMRs overlapped with chromatin states 18% Polyphagia 16% 13% 10% 7% 0 102030 0% 0.0

-Log10(FDR) P0 ESC DKO E14.5 E15.5 E16.5

Non-DMR Non-DMR Week 3 Week 8 Hypo-DMRHyper-DMR Hypo-DMRHyper-DMR

F DNA methylation H3K27ac H3K4me1 Accessibility Chromatin accessibility by single cell ATAC-seq

Hypo-DMRs (5,686) overlapped with kidney enhancers ESC E14.5E15.5E16.5P0 AdultControlDKO E15.5 P0 W8 E15.5 P0 W8 E15.5 P0 W8 PT_1 PT_2 PT_3 PT_5 PT_S3DCT DCT_CDCD LOH_2LOH_3GlomerularPodocytes

Fetal enhancer Only (3,321, 58.4%)

Fetal & adult Enhancer (1,864, 32.8%)

Adult enhancer

Only DNA ATAC-seq signal RPKM methylation (RPKM) (501, (ChIP - Input) -5k 5k -5k 5k -1.5k 1.5k -1.5k 1.5k 8.8%) 0.0 0.25 0.5 0.75 1.0 DMR DMR -5 0 5 10 15 DMR DMR 0.0 0.25 0.5

Figure 3. Base-resolution methylome analysis of isolated tubule cells in control and KspCreDnmt3a/3b mice. (A) Experimental design. (B) Volcano plot, x axis shows differentially methylated regions (20% change in at least five CpG of any fragment) and y axis shows the number of DMRs. (C) Methylation level of 17,578 DMRs in Cdh161 cells in control (y axis) and KspCreDnmt3a/3b (x axis). Color (higher red) indicates the number of DMRs in that group. (D) Mouse phenotypes enriched by hypo-DMRs and hyper-DMRs in

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Cre Next, we analyzed whether Ksp Dnmt3a/3b mice show performed RRBS on whole-kidney samples obtained from 3- Cre Cre alterations in response to injury. To model AKI, we treated week-old control and Ksp Dnmt3a/3b and Six2 Dnmt3a/ Cre control and Ksp Dnmt3a/3b mice with cisplatin. Serum 3b mice (Figure 2A). On average, RRBS quantified methyl- BUN level of cisplatin-treated mice was significantly in- ation levels of approximately 2.4-million CpG sites represent- creased, confirming the injury. Serum BUN level was lower ing approximately 10.8% of CpG sites in the mouse genome. Cre in Ksp Dnmt3a/3b mice when compared with cisplatin- Using stringent criteria (of q value ,0.01 and methylation treated controls (Figure 1F). Tubule injury markers such as difference .20%), we identified 7184 DMRs in kidneys of Cre kidney injury molecule 1 (Kim-1) and Lipocalin-2 (Lcn2)were Six2 Dnmt3a/3b mice and 1457 DMRs in kidneys of Cre Cre lower in Ksp Dnmt3a/3b mice compared with controls Ksp Dnmt3a/3b mice (Figure 2B). As expected, most regions (Figure 1G). The macrophage marker Cd68 and cytokine showed lower methylation levels in Dnmt3a/3b knockout mice Cre Ccl2 were higher in cisplatin-treated animals but they were (94.3% of the DMRs in Six2 Dnmt3a/3b and 71.3% in Cre Cre less prominent in Ksp Dnmt3a/3b mice (Figure 1G). Kidney Ksp Dnmt3a/3b mice) (Figure 2B), indicating that Dnmt3a segment–specific marker genes, such as the proximal tubule and Dnmt3b play a key role in de novo methylation. Regions marker Slc34a1 and loop of Henle marker Slc12a1 were that failed to gain methylation in the Dnmt3ab/3b double decreased after cisplatin treatment and these changes were knockout kidneys were mostly fetal kidney enhancers Cre comparable in Ksp Dnmt3a/3b mice (Figure 1H). We also (Supplemental Figure 4). analyzed the injury response in the folic acid–induced kidney Next we examined global methylation patterns at birth (P0) fibrosis model. We did not observe significant differences be- and at 3 weeks of age (P21) (Figure 2B), which is the time Cre tween control and Ksp Dnmt3a/3b mice in the folic when nephron development ceases in mice. As reported ear- acid–induced kidney fibrosis model (Supplemental Figure 2). lier, there was a significant gain in CpG sites methylation Because Chd16 expression is segment specific and expresses (69.1%) during this period (P0–P21). This is consistent with at later stages of development, we next genetically deleted the terminal differentiation of cells during this period. Develop- Cre Dnmt3a and Dnmt3b in nephron progenitors using the Six2 mental DMRs failed to gain methylation in Dnmt3a/3b knock- Cre Cre 2 mice (Six2 Dnmt3a/3b or Six2 DKO) (Supplemental out mice (chi-squared test P51.05310 33; Figure 2C). This Cre Figure 3A). Six2 is expressed at E11.5, and it labels the self- effect was more pronounced in the Six2 Dnmt3a/3b animals Cre renewing nephron progenitors that give rise to all nephron compared with Ksp Dnmt3a/3b mice. There was a limited Cre Cre epithelia5 (Supplemental Figure 1, A, B, and D). Six2 Dnmt3a/ overlap between hypo-DMRs observed in Six2 Dnmt3a/3b Cre 3b mice were born at the expected Mendelian ratio. Kidney sec- and Ksp Dnmt3a/3b mice (Figure 2, D and E), indicating Cre tions of 3-week-old Six2 Dnmt3a/3b mice showed no obvious that the targets of Dnmt3a and Dnmt3b are spatially and tem- structural abnormalities (Supplemental Figure 3B). Transcript porally different. In summary, cytosine methylation was severely levels of kidney segment markers (Slc34a1, Slc12a1, Slc12a3,and reduced in kidneys of mice lacking de novo methyltransferases Cre Cre Cre Aqp2)in3-week-oldSix2 Dnmt3a/3b mice were comparable such as Six2 Dnmt3a/3b and Ksp Dnmt3a/3b. to littermate controls (Supplemental Figure 3C). Taken together, Dnmt3a and Dnmt3b appeared dispensable in adult kidney tu- Dnmt3a and Dnmt3b Are Necessary for de Novo bule cells at baseline. However, they seem to confer some resis- Methylation of Fetal-Specific Enhancers tance to AKI, but not to kidney fibrosis. For accurate cell type–specific, base-resolution methylome analysis, we performed WGBS on sorted Cdh16 (Ksp-cadherin)- Dnmt3a and Dnmt3b Play Important Roles in positive cells (Figure 3A). Cells were isolated from kidneys of 3- Cre Establishing Methylation Patterns during Kidney week-old Ksp Dnmt3a/3b and control mice. The methylation Development patterns of Cdh16-positive cells showed strong concordance To examine the contribution of Dnmt3a and Dnmt3b to kid- with whole-kidney methylation (Supplemental Figure 5A). To ney cytosine methylation changes during development, we identify DMRs, we first segmented the genome into 980,525

KspCreDnmt3a/3b based on GREAT for functional annotation. (E) The overlap between hypo-DMRs and hyper-DMRs in KspCreDnmt3a/ 3b mice and different chromatin states in fetal and adult kidneys. Chromatin states from fetal and adult kidneys were used to classify the genome location of each segment by WGBS. (F) Functional annotation of hypo-DMRs (5686, lost methylation in Dnmt3a/3b knockout mice). First panel showed the number and fraction of hypo-DMRs overlapped with fetal and/or adult enhancers. Second panel shows the methylation levels of hypo-DMRs during kidney development, control, and KspCreDnmt3a/3b. The data were ordered according to the methylation level in control kidneys. Third panel shows density of chromatin mark (associability, H3K27ac, and H3K4me1) in each hypo-DMR and its flanking regions (5 kb) in fetal kidney (E15.5, P0) and adult kidney (8 weeks old). The last panel shows the chromatin accessibility by single cell assay for transposase-accessible chromatin using sequencing single cell (ATAC-seq) in adult kidney (8-week-old) cell types. (G) Global CpG methylation levels in kidney enhancers and promoters at different stages of development and in Dnmt3a/3b knockout mice. Abn., abnormal; CD, collecting duct; DCT, distal tubule; DKO, double knockout; ESC, embryonic stem cell; FDR, false discovery rate; LOH, loop of Henle; Podo, podocyte; PT, proximal tubule; RPKM, reads per kilobase of transcript per million mapped reads; W8, week 8.

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A Fetal Adult Control DKO Fetal Adult DKO

Developmental DMRs DKO DMRs DMRs gaining methylation in adult kidney and losing methylation in Motif Type Consensus P Motif Type Consensus P Gain methylation in adult (23,640) Lose methylation in DKO (13,276) DKO kidney cells (n=1,951) Hoxc9 Homeobox 1.00E-287 Dlx3 Homeobox 1.00E-63 Motif Type Consensus P PBX2 Homeobox 1.00E-188 Hoxc9 Homeobox 1.00E-44 Hoxc9Homeobox 1.00E-30 Six2 Homeobox 1.00E-136 PBX2 Homeobox 1.00E-34 Six2 Homeobox 1.00E-25 Six1 Homeobox 1.00E-132 Six2 Homeobox 1.00E-24 Dlx3 Homeobox 1.00E-23 Dlx3 Homeobox 1.00E-131 Six1 Homeobox 1.00E-16 Lhx1 Homeobox 1.00E-22 Lose methylation in adult (23,293) Gain methylation in DKO (4,302) CDX4 Homeobox 1.00E-20 NF1 CTF 1.00E-181 EWS:ERGETS 1.00E-16 PBX2 Homeobox 1.00E-19 HNF1b Homeobox 1.00E-102 Gata6 Zf 1.00E-10 Six1 Homeobox 1.00E-17 EWS:ERG ETS 1.00E-43 Etv2 ETS 1.00E-09 Lhx3 Homeobox 1.00E-15 PU.1 ETS 1.00E-26 ELF3 ETS 1.00E-06 Lhx2 Homeobox 1.00E-13 Etv2 ETS 1.00E-24 PU.1 ETS 1.00E-05 Pdx1 Homeobox 1.00E-13 >100kb B E 100% NPC Six2 peaks Shuffled regions Enhancer Promoter

80% 2,946 bp 122,489 bp Hypo-DMR 60% CpG island 40% Gene E15.5 with enhancer Percentage of 20% P0 segments overlapped WGBS 0% Adult Fetal Fetal Adult Not Control enhancer & adult enhancer kidney only enhancer only enhancer DKO E15.5 H3K27ac P0 C p < 2.2e-16 p < 2.2e-16 Adult 0.6 0.6 NPC Six2 NPC 0.3 0.3 NPC

0.0 0.0 F NPC Six2 peaks Shuffled regions 1.00 -0.3 -0.3 (DKO - Control) 0.75 Methylation difference Methylation difference 0.50

-0.6 (adult kidney – fetal kidney) -0.6 0.25 methylation NPC Shuffled NPC Shuffled Global CpG Six2 regions Six2 regions 0.00 peaks peak P0 P0 ESC ESC E14.5 E15.5 E16.5 E14.5 E15.5 E16.5

D NPC Six2 peaks Shuffled regions G Week 3 Week 8 Week 3 Week 8 0.6 NPC Six2 peaks Shuffled regions 8.9e-16 1.1e-09 0.073 0.3 1.0

0.0 0.5 0.0 -0.3

(DKO - Control) 56.1% 26.9% -0.5 CpG methylation Methylation difference -0.6 Correlation between

Six2 expression and -1.0 -0.6-0.3 0.0 3.0 6.0 -0.6-0.3 0.0 3.0 6.0 Fetal Fetal Adult Methylation difference enhancer & adult enhancer (adult kidney – fetal kidney) only enhancer only

Figure 4. KspCreDnmt3a/3b DMRs are enriched for developmental transcription factor binding. (A) Transcription factor motif en- richment (HOMER) of DMRs, during kidney development (P0–P21), in control versus KspCreDnmt3a/3b kidneys. Fragments that gained methylation during development but failed to gain methylation in Dnmt3a/3b knockout mice. (B) The degree of overlap between DMRs that are in enhancer regions and on Six2 binding sites in nephron progenitors (NPC) from ChIP-seq (cyan) compared with background (gray). (C) Methylation differences in control versus KspCreDnmt3a/3b kidneys (left) or adult versus fetal kidneys (right) that overlap with Six2 binding versus background shuffled region. (D) Scatter plot of methylation changes of Six2 binding sites in NPCs, during kidney development (x axis) and in control versus KspCreDnmt3a/3b (y axis). The red dots represent the fragments gaining

JASN 31: ccc–ccc,2020 Role of Dnmt3a/Dnmt3b in Kidney 9 BASIC RESEARCH www.jasn.org fragments, each of them containing at least five CpG sites, as enhancers, we found that 58% were fetal specific whereas only established in the SMART method (Supplemental Figure 5B).24 a small fraction (8.8%) of hypo-DMRs were annotated as en- The differential methylation analysis identified 17,578 fragments hancers only in the adult mouse kidney (Figure 3F, (DMRs) showing at least 20% change in methylation between Supplemental Figure 5C). Hypo-DMRs gained methylation, Cre Ksp Dnmt3a/3b mice and controls (Figure 3B, Supplemental losing enhancer marks and chromatin accessibility in the adult Table 2). Consistently, .75% (13,276) of DMRs showed a lower kidney (Figure 3F, Supplemental Figure 5, E and F). These Cre methylation level (hypo-DMRs) in kidneys of Ksp Dnmt3a/3b results revealed Dnmt3a and Dnmt3b were necessary for de mice. Loci with intermediate (40%–80%) methylation level were novo methylation and decommissioning of fetal-specific Cre mostly affected in the Ksp Dnmt3a/3b mice (Figure 3C). Func- enhancers. tional annotation of these DMRs indicated that demethylation events tended to be close to genes associated with morphogen- Dnmt3a and Dnmt3b Are Required for esis and kidney development (Figure 3D). Decommissioning of Fetal Enhancers Bound by To define regions that are specifically altered by Dnmt3a- Developmental Transcription Factors and Dnmt3b-mediated differential methylation, we mapped Because DMRs were enriched on developmental-enhancer re- DMRs to functional regulatory elements. Kidney-specific gions, we were interested to understand whether we could functional regulatory elements such as enhancers and pro- identify critical transcription factors associated with these moters were annotated by integrating multiple histone ChIP sites. We used transcription factor motif analysis established data obtained from fetal and adult mouse kidney samples.42 by HOMER35 to analyze DMRs from the WGBS data set. Upon We found that Dnmt3a-andDnmt3b-mediated DMRs were comparing regions that gained methylation during develop- enriched on enhancers, but hardly ever observed on promoter ment, we found a measurable enrichment for homeobox tran- regions (Figure 3E, Supplemental Figure 5C). When we com- scription factors, including Six2 which is known to play a key pared fetal and adult kidneys, we found that loci with lower role in kidney development (Figure 4A). Through examining Cre methylation (hypo-DMRs) in Dnmt3a/3b knockout mice were DMRs identified in kidneys of Ksp Dnmt3a/3b mice, we strongly enriched on fetal-enhancer regions (38% versus 13%, again found enrichment for homeobox transcription factors, 2 chi-squared test P,2.2310 16), and to lesser degree on re- such as Six2 binding sites (Figure 4A). The overlap between gions identified as transcribed regions in fetal kidneys the developmental DMRs and Dnmt3a/3b knockout DMRs (Figure 3E). showed significant enrichment for kidney developmental Next, we wanted to understand the fate of the fetal en- transcription factor binding sites including Hoxc9, Six2, hancers and the role of methylation. Upon analyzing cytosine Six1,andDlx3 (Figure 4A). Six2, a kidney developmental tran- methylation on a genome-wide scale during kidney develop- scription factor, is required for nephron progenitor mainte- ment, we found that adult promoters and enhancers showed a nance.7,43 These results indicate that a good portion of fetal mild, gradual decline in global methylation level, indicating enhancers that are decommissioned by Dnmt3a/3b are bound their openness was determined earlier (Figure 3G). On the by Six2. other hand, fetal enhancers and promoters gained significant Because motif analysis cannot distinguish among closely methylation during the P0–P21 time period. Kidneys of related transcription factors, we next specifically examined Dnmt3a/3b knockout mice failed to gain methylation, consis- Six2 binding sites identified in NPCs by ChIP-seq.37 The tent with their role as de novo methyltransferases (Figure 3G). Six2 peaks in NPCs were affected by methylation because To further explore the function of Dnmt3a/3b in kidney they were enriched for fetal hypo-DMRs (Figure 4B) com- development, we identified 3334 DMRs that showed methyl- pared with a shuffled background. More than half (56%) of ation changes during development and also showed changes the fragments that overlapped with NPC Six2 peaks gained in Dnmt3a/3b knockout mice. Most (58.5%) of these shared methylationindevelopmentbutfailedtobemethylatedin Cre DMRs underwent de novo methylation in development but Ksp Dnmt3a/3b mice(Figure4,CandD,Supplemental failed to increase their methylation in Dnmt3a/3b knockout Figure 7A). These fragments were localized to genes that are mice, and were enriched in fetal-enhancer regions known to play roles in kidney development. For example, Six2 (Supplemental Figures 5, C and D, and 6). When focused peaks in nephron progenitors overlapped with the distal en- our analysis on the hypo-DMRs that overlapped with kidney hancer of Pax2 which is a critical regulator of kidney methylation in adult kidneys, but not in KspCreDnmt3a/3b mice. (E) IGV genome browser view of the Pax2 region (chromosome 19: 44729363–44851852). DNA methylation changes in hypo-DMRs (lower methylation in KspCreDnmt3a/3b), note the methylation pattern in the developing mouse kidney (E15.5, P0), wild-type and Dnmt3a/3b knockout (DKO) mice, H3K27ac enhancer mark, and Six2 binding. Note the failure to increase in methylation of a fetal-enhancer region. (F) Global CpG methylation in NPC Six2 peaks and shuffled regions at different stages of kidney development. (G) Correlation between gene expression of Six2 and CpG methylation of segments overlapped with NPC Six2 peaks and shuffled regions. Wilcoxon signed rank sum test was carried out to calculate the significance of difference between NPC Six2 peaks and shuffled regions, and P values was provided for each comparison. ESC, em- bryonic stem cell.

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ACBiological processes Epithelial cell differentiation Control RNA Programmed cell death sequencing

Epithelium development Up KspCre DKO (RNA-seq) Collecting duct development Kidney development 3-week-old Kidney Wound healing Alpha-amino acid catabolic process B Cellular amino acid catabolic process Down-regulated (27) Up-regulated (122) Reponse to wounding Down 18 Anion transport 15 Mouse phenotype 12 Renal/urinary system phenotype Abnormal renal/urinary system physiology

9 Abnormal urine osmolality Up Abnormal renal/urinary system morphology 6 Abnormal kidney physiology -log10(FDR) Abnormal thrombosis 3 Heart left ventricle hypertrophy Decreased t cell proliferation 0 Homeostasis/metabolism phenotype Down -10 -5 0510 Abnormal blood homeostasis Log2 transformed fold change 0246 -Log10(p value) D TAD-constrained map of enhancer-promoter associations F DKO Hypo-DMR Gene Promoter Enhancer DMR E15.5 P0 WGBS Adult Control eDMR-DEG DKO associations E15.5 Chromatin state P0 Adult Control 31 eDMR-DEG associations Control (31 eDMRs ~ 21 DEGs) RNA-seq DKO Spearman’s rho = -0.4, p = 0.02 8 DKO chr16:89705992-90036540 chr16:89943409-89945895 6 Pdia2 E Tiam1 chr16:89944403-89944892 24 0.8 4 Spearman’s Tiam1 rho Positive 2 16 0.6 Negative Elf3 0 0.2 (TPM) 8 0.4 Expression difference Log2 (DKD/Wild type) 0.4 Spearman’s rho = -0.84 CpG Methylation Tiam1 expression p-value = 0.0027 2 0.6 0 0.2 0.8 P0

-4 E14.5 E15.5 E16.5 -0.40 0.4 Methylation difference Week 3 DKO (DKO - Control) eek 3 Control W

Figure 5. Dnmt3a-andDnmt3b-mediated methylation represses developmental genes in late development stage. (A) Schematics of the experiments. (B) Gene expression changes in KspCreDnmt3a/3b mice. Volcano plot; x axis shows fold-change difference, and y axis shows statistical difference (2log false discovery rate [2logFDR]). Red dots represent genes with higher expression in KspCreDnmt3a/ 3b, whereas blue dots represent genes with lower expression. (C) Function enrichment (biologic processes and mouse phenotype) analysis of differentially regulated genes. Red colors represent genes with increased expression, whereas blue represent decreased expression. (D) Enhancer and promoter associations obtained from topologically associating domain (TAD)–constrained maps. eDMRs represent enhancers that also showed differential methylation. The association (Spearman rank correlation coefficient) between eDMRs and associated genes and significance was calculated and showed. (E) Methylation (of eDMR) and expression correlation of Tiam1 locus

JASN 31: ccc–ccc,2020 Role of Dnmt3a/Dnmt3b in Kidney 11 BASIC RESEARCH www.jasn.org development.44 This enhancer showed low methylation levels associations(D.U.Gorkin,I.Barozzi,Y.Zhang,A.Y.Lee, in the fetal kidney and its methylation level increased in the B.Li,Y.Zhao,et al., unpublished observations). We identi- adult kidney (Figure 4E, Supplemental Figure 7A). In contrast fied 31 associations between 31 eDMRs and 21 DEGs to the wild-type mice, this enhancer failed to gain methylation (eDMR~DEG). The methylation changes in these eDMRs Cre in kidneys of the Ksp Dnmt3a/3b mice and the regions re- were associated with changes in their target gene expression mained similar to those in fetal kidneys. Furthermore, chro- (Figure 5D, Supplemental Figure 9A, Supplemental Table 3). matin conformation capture contact matrices revealed Most of the eDMR-DEG associations (84%) were direction interactions between this enhancer and the Pax2 locus consistent, such as lower methylation was associated with (Supplemental Figure 7B). Integrative analysis revealed that higher expression (Figure 5D). For example, an eDMR on Six2 binding sites had lower methylation levels in fetal kidneys the Tiam1 locus showed lower methylation in Cre and gained methylation after birth (Figure 4F), indicating an Ksp Dnmt3a/3b miceandanincreaseintranscriptexpres- interaction between Six2 binding and methylation changes. sion of Tiam1 (Figure 5, E and F, Supplemental Figure 9B). For example, pioneering transcription factors not only play Tiam1 was reported to play a role in Wnt signaling and roles in opening closed chromatin sites during development, epithelial-mesenchymal transition,46,47 and it is mostly si- but their binding footprint can be observed even after the lenced in adult kidney tubules. Overall, the effect of transcription factor is no longer expressed.45 To explore this Dnmt3a-andDnmt3b-mediated methylation changes on hypothesis, we calculated the correlation of fragment methyl- gene expression modulation was modest, and mostly af- ation and Six2 expression during kidney development. The fected genes involved in kidney development. Six2-bound fragment methylation showed negative correla- tion with Six2 expression, which was particularly obvious Dnmt3a- and Dnmt3b-Mediated Methylation Changes for fetal enhancers (Figure 4G). For example, methylation of Are Enriched for Kidney Disease Genetic Risk Loci a locus on chromosome 7 was significantly negatively corre- GWASs have identified nucleotide variations that are en- lated (Spearman r520.97, P value50.0002) with the expres- riched in patients with CKD. Previously, we showed that a sion of Six2 (Supplemental Figure 8). This region included good portion of such loci are enriched on enhancer regions Six2 binding sites that failed to gain methylation in Dnmt3a/ in the adult kidney,10 however, more than half of GWAS loci 3b knockout mice. These results indicated that DNA methyl- remain unannotated. Here, we hypothesized that kidney ation, mediated by Dnmt3a and Dnmt3b, preferentially disease risk loci might be specific to the developmental affected enhancer regions bound by Six2 during kidney devel- stage, i.e., might be active in the fetal tissue, explaining opment. The methylation of these sites correlated with Six2 the lack of regulatory annotation in the adult human kid- expression. ney. As we showed earlier, such fetal enhancers are specif- ically methylated and decommissioned by Dnmt3a/3b in Cre Transcriptional Changes Observed in Ksp Dnmt3a/ adult kidney. 3b Mice As a first step, we overlapped the entire human GWAS Next, we performed unbiased gene expression analysis by catalog and DMRs identified during development. We found Cre RNA sequencing of kidneys of control and Ksp Dnmt3a/3b variants associated with kidney function (Figure 6A) and mice (Figure 5A). We identified 149 genes that passed the other kidney-associated traits were specifically localized to significance threshold for differential expression (DEGs). kidney developmental DMRs. Next, we narrowed the DMRs Consistent with the demethylation events, most (82%, 122/ only those were found to be methylated by Dnmt3a and 149) DEGs showed an increase in their expression in Dnmt3b. These DMRs showed a strong enrichment for kid- Cre Ksp Dnmt3a/3b mice, including several kidney developmen- ney function–associated traits (Figure 6A). Functional an- tal genes such as Wnt4 and Wnt9b (Figure 5B). Functional notation revealed that 68% of DMRs that overlapped with annotation indicated that DEGs were enriched for kidney de- kidney function–associated SNPs were fetal kidney–specific velopment and epithelial differentiation functions enhancers, which was significantly higher than expected by 2 (Figure 5C). chance (chi-squared test P51.083 10 12;Figure6B, Next, we tested whether enhancer methylation correlates Supplemental Figure 10A, Supplemental Table 4). Cross- with gene expression changes. To identify targets of enhancer species comparison indicated these fetal kidney enhancers DMR (eDMR), we obtained topologically associating, were conserved between mouse and human, both in se- domain-constrained maps for accurate enhancer-promoter quence and in their methylation patterns (Supplemental

in kidney development and in KspCreDnmt3a/3b mice. Transcripts per million (TPM) was used for quantification of RNA expression. Spearman rank correlation coefficient and significance was calculated and showed. (F) IGV genome browser view of the Tiam1 locus (chromosome 16: 89944403–89944892), including WGBS tracks during kidney development E15.5–P0, chromatin state and gene expression by RNA sequencing in adult and KspCreDnmt3a/3b mice. The right panel is a zoom-in region of the eDMR. DKD, diabetic kidney disease; DKO, double knockout.

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AD SNPs associated with eGFR (eGFR-SNPs) DMRs Kidney function-related Morris traits based on GWAS Wuttke 311 Combined 26,637 1,418 13 catalog eGFR-SNPs 3,510 13,327 DKO DMRsDevelopment 4,934 Lift over to Mouse (mm10) Blood urea nitrogen levels 3,124 Glomerular filtration rate (creatinine) 7,923 (29.7%) Hellwege Glomerular filtration rate in non diabetics (creatinine) eGFR-SNPs Hemoglobin conserved in mouse Hemoglobin levels Idiopathic membranous nephropathy 2,901 WGBS segments Overlap with WGBS segments Renal function-related traits (eGRFcrea) Renal function-related traits (sCR) Fetal DKO DMR 4,886 (61.7%) Serum alkaline phosphatase levels enhancer 9 17 eGFR-SNPs Thyroid stimulating hormone levels 4 5 overlapped with Vitamin D levels 553 83 65 2,901 WGBS segments -log10(p-value) Development DMR 063

B Other CEFNeither Other Neither Transcript K4me1 Transcript K4me1 Enhancer K27ac Enhancer K27ac Promoter K27ac&K4me1 Promoter K27ac&K4me1 30 50 200 900

25 750 40 150 20 600 30 15 100 450 68% 20 10 300 39% 59% 51% Fraction of DMRs 50 49% 34% 5 10 Fraction of enhancers 150 28% 23% overlapped with eGFR-SNPs Fraction of DMRs overlapped with GWAS SNPs associated overlapped with eGFR-SNPs with GWAS SNPs associated with kidney function-related traits with kidney function-related traits 0 Fraction of enhancers overlapped 0 0 0

Fetal Adult E15.5 Fetal Adult E15.5 Week 8 Week 8

DMRs overlapped with GWAS SNPs Kidney development GHassociated with kidney function-related traits 140 0.8 Spearman’s rho = -0.58, p = 0.0018 Speraman’s rho = –1.0 HNF1A Fetal enhancer only 120 p-value = 0.0028 0.6 0.2 BCAR3 Fetal & adult enhancer 100 A1CF Adult enhancer only 80 0.1 Non enhancer 0.4

(TPM) 60 40

Six2 expression 0.2 0.0 CpG methylation 20 GNAS 0 0 (DKD - Control) -0.1 BCAS3 P0 Methylation difference E14.5 E15.5 E16.5 UNCX Week 3 Week 8 -0.2 in human chronic kidney disease Uncx chr7:24389472-24389973 -0.6 -0.3 0.0 0.3 0.6 Methylation difference in mouse kidney development (Adult - Fetal)

Figure 6. Dnmt3a/3b-methylated regions harbor kidney disease risk loci. (A) Enrichment of human disease risk loci (obtained from GWAS catalog) in developmental and Dnmt3a/3b double knockout DMRs. Only the significantly enriched human kidney dis- ease–related traits were shown, and the significance was represented by color from white to dark blue. (B) Genomic location of DMRs overlapping with kidney disease risk variants. Chromatin states from fetal and adult kidneys were used to classify the genome location of each DMR. (C) Histone-modification transition from fetal mouse kidney to adult mouse kidney in enhancers overlapping with kidney function associated traits. ChIP-seq peaks of H3K27ac and H3K4me1 were used to classify the transition pattern of each enhancer.

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Figure 10, B and C). Specifically, we integrated the different the methylation of this GWAS region. Consistent with the enhancer marks (H3K27ac and H3K4me1) in fetal (E15.5) notion that this region is a fetal-specificactiveenhancer, and adult (week 8) kidneys, and identified 51 GWAS loci that Uncx/UNCX wasexpressedinfetalbutnotinadultkidney overlapped with kidney enhancers (Supplemental Table 5). (Figures 6H and 7A, Supplemental Figure 10E), indicating Most of these enhancers (59%) were positive for both en- the important role of Dnmt3a and Dnmt3b in decommis- hancer marks (H3K27ac and H3K4me1) in the fetal stage, sioning fetal enhancers. Finally, when compared with but only 28% of them remained positive for both marks in healthy kidney samples, kidney tubules from patients with the adult kidney (chi-squared test P50.0052) (Figure 6C, diabetic kidney disease showed strong similarities to kidneys Supplemental Figure 10A). To confirm the GWAS cata- of Dnmt3a/3b knockout mice, such as the loss of cytosine log–based finding (which only reports the top associated methylation of this region (Figure 7B). In addition to the SNPs), we combined a comprehensive list of 26,637 SNPs UNCX/Uncx locus, we also examined the Hoxd/HOXD that were significantly associated with eGFR (eGFR-SNPs) locus. Again, we found a similar pattern, such as conserva- inthemostrecentGWASstudies9,40,41 (Figure 6D). More tion between the human and mouse locus, Dnmt3a/3b- than a half (51%) of DMRs overlapped with eGFR-SNPs mediated methylation, and decommissioning of fetal were localized to fetal enhancers, and 49% of enhancers enhancers (Figure 7, C and D, Supplemental Figure 10F). that overlapped with eGFR-SNPs were enriched for enhancer In summary, our results indicate that Dnmt3a-and marks (H3K27ac and K3K4me1) in fetal kidneys (Figure 6, E Dnmt3b-mediated methylation of fetal enhancers are en- and F). These results raise the possibility that genetic variants riched on kidney disease risk loci. in fetal enhancers decommissioned by Dnmt3a and Dnmt3b contribute to human kidney disease development. To further understand the clinical significance of our find- DISCUSSION ings, we analyzed the methylation of DMRs that overlapped with GWAS SNPs associated with kidney function–related Here, via integrating mouse genetic studies and genome-wide traits in microdissected kidney tubule samples obtained methylome and expression profiling, we elucidated the role of from healthy subjects and patients with diabetic kidney dis- Dnmt3a and Dnmt3b in renal tubule epithelium in develop- ease.14 Methylation changes during kidney development were ment, maturation, adult, and diseased mouse kidneys. Using significantly negatively correlated with methylation changes base-resolution temporal profiling, we described dynamic observed in diabetic kidney disease (Figure 6, G and H), sug- changes of DNA methylation during kidney development. gesting the methylation patterns established during develop- Globally, we observed the largest decline in global methylation ment were either reversed in diabetic kidney disease or failed level between embryonic stem cells and renal progenitors, to establish during development. whereas the greatest increase in methylation was observed For example, GWASs have revealed that nucleotide vari- during postnatal maturation (P0–P21). In mice, during the ants nearby UNCX were significantly associated with kidney first 3 weeks, new nephrons are formed, epithelial cells pro- functions (Supplemental Figure 10D). The methylation pat- liferate, and the kidney enlarges drastically.48 Regions that act tern of this locus in healthy human kidneys was similar to the as enhancers in the adult kidney, show very small and gradual mouse kidney, including a large area of lowly methylated changes in their methylation level during development. region (Figure 7, A and B). Kidney function–associated Changes in methylation, on the other hand, are associated GWAS variants were localized to a fetal enhancer which with alterations in transcript expression that again occur in showedtheactiveenhancermarksH3K27acandH3k4me1 the postnatal stage. These results indicate that the epigenetic in fetal kidneys. Although the region remained minimally state of these regions is likely established early during kidney H3K4me1 positive in the adult kidney, this region was no development and their postnatal expression is mostly tran- longer positive for H3K27ac, indicating that it was not an scriptionally controlled. active enhancer in adult kidneys. This region showed an in- Our results indicated that kidney-specific fetal enhancers crease in methylation level during development and failed to underwent important changes during postnatal kidney devel- gainmethylationinabsenceofDnmt3a and Dnmt3b,indi- opment. We observed a substantial increase in enhancer cating the key role of Dnmt3a and Dnmt3b in establishing methylation after birth (P0–P21). Histone-modification data

(D) Integration of eGFR-associated SNPs (eGFR-SNPs) from recently published GWAS.9,40,41 The combined eGFR-SNPs after the lift over to mouse genome (mm10) were overlapped with WGBS segments. (E) Genomic location of DMRs overlapped with eGFR-SNPs. (F) Histone-modification transition from fetal mouse kidney to adult mouse kidney in enhancers overlapping with eGFR-SNPs. (G) DNA- methylation changes during mouse kidney development and human diabetic kidney disease (DKD). Spearman rank correlation co- efficient and significance was calculated and showed. (H) Gene expression of Uncx and CpG methylation of eDMR overlapped with kidney function–associated SNPs during kidney development from E14.5 to 8 weeks after birth. sCR, serum creatinine; TPM, transcripts per million; eGFRcrea, estimated glomerular filtration rate based on serum creatinine levels.

14 JASN JASN 31: ccc–ccc,2020 n 34e ehne ak) n eeepeso yRAsqecn RAsq.(oetefiueo ehlto fteenhancer the of methylation of failure the (Note (RNA-seq). sequencing RNA by of expression absence in gene region and marks), (enhancer H3K4me1 and CCGnm rwe a sdt dniytecnevdrgo ewe ua n mouse and human between region conserved the identify to used was Browser Genome UCSC and kidney developing the in (WGBS) patterns methylation by followed variants JASN mouse 7. Figure 31: Uncx ccc eei n pgntcfaue of features epigenetic and Genetic – Phastcons B A eGFR-SNPs Renal diseaseSNPs CpG island Gene ccc eGFR-SNPs Renal diseaseSNPs CpG island Gene ou crmsm cr] 139515562 [chr5]: 5 (chromosome locus WGBS WGBS RNA-seq H3K4me1 H3K27ac Six2 WGBS Diabetic disease Normal ,2020 kidney Kidney Control E16.5 E15.5 E14.5 E15.5 E15.5 E15.5 Adult Adult Adult Adult −log10(p value) DKO 10 20 30 40 50 60 P0 P0 P0 P0 0 Dnmt3a/3b [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -20] [0 -20] [0 -20] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] r 2 0.2 0.4 0.6 0.8 chr5:139515562-139580379 (mm10) p:2 CpG:_82 CpG:_26 chr7:1239604-1308516 (hg19) 4930500l23Rik rs1123164 .()IVgnm rwe iwo h human the of view browser genome IGV (B) ). s136 s910 s759 rs4724828 rs7785293 rs6951209 rs1123164 Renal_function-related_traits_(BUN) Renal_function-related_traits_(BUN) s6191r6529r4285r7823r4287rs73670555 rs4724817 rs7785293 rs4724805 rs6951209 rs76210971 p:6 p:14CpG:_26 CpG:_124 CpG:_69 Uncx CpG:_923 UNCX UNCX rs62435145 Hemoglobin – and 3507) h o ae hw WSadkde ucin(eGFR) function kidney and GWAS shows panel top The 139580379). HOXD ua inydsaeascae oi A G eoebosro the of browser genome IGV (A) loci. associated disease kidney human eGFR-SNPs Renal diseaseSNPs CpG island Gene eGFR-SNPs Renal diseaseSNPs CpG island Gene Phastcons C D WGBS WGBS RNA-seq H3K4me1 H3K27ac Six2 WGBS Diabetic Control disease kidney Normal Kidney E16.5 E15.5 E14.5 E15.5 E15.5 E15.5 Adult Adult Adult Adult −log10(p value) DKO P0 P0 P0 P0 10 15 20 UNCX 0 5 Dnmt3a/3b [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -10.00] [0 -20] [0 -20] [0 -20] [1.000 -20] [1.000 -20] [1.000 -20] [0 -20] [0 -20] [0 -20] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] 235 rs72919076 6253455 [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] [0 -1.00] ou cr:1239604 (chr7: locus chr2:176872325-177132115 (hg19) s2173r7932 s2244r7976 s2116rs72916158 rs72916126 rs72927167 rs72923454 rs72923424 rs72914763 chr2:74593010-74824038 (mm10) CpG:_77 rs72919076 Estimated_glomerular_filtration_rate_(eGFR)_ p:8 p:2 p:9 p:3 CpG:_84 CpG:_35 CpG:_99 CpG:_27 CpG:_84 Evx2 Evx2 ncotkidney, knockout od2Hx9Hx3Haglr Hoxd3 Hoxd9 Hoxd12 UNCX Estimated_glomerular_filtration_rate CpG:_63 oeof Role s2244r7978 rs57225327 rs72927180 rs72923454 OD0HOXD3 HOXD10 rs187355703 www.jasn.org / p:2 CpG:_141 CpG:_25 Dnmt3a/Dnmt3b Uncx – 381) itoe rmthe from Lift-over 1308516). ou.Lcsomshowing LocusZoom locus. Six2 AI RESEARCH BASIC idn,H3K27ac binding, nKidney in r 2 0.2 0.4 0.6 0.8 – associated 15 BASIC RESEARCH www.jasn.org indicated that the change in methylation (at birth) was asso- increased resistance to AKI, which requires rapid prolifer- ciated with a loss of H3K27ac, a histone mark that defines ation and cell differentiation. active enhancers. Dnmt3a and Dnmt3b play a critical role in Here we show that developmental enhancers, whose de methylation of these fetal enhancers and we identified thou- novo methylation is specifically mediated by Dnmt3a and sands of enhancer regions whose methylation was not estab- Dnmt3b, are enriched for kidney disease genetic risk loci. lished in absence of Dnmt3a and Dnmt3b. These enhancers These regions are annotated as active enhancers in the fetal show intermediate methylation levels at the fetal stage. Al- kidney, many bound by Six2, a critical kidney developmen- though their methylation increases to the adult stage, tal transcription factor. However, these regions are no they do not seem to gain full methylation in the normal longer annotated as active enhancers in the adult stage. adult mouse kidney. Previously, these loci have also been Fetal enhancer methylation level increases during develop- called “vestigial enhancers.”49 Here we showed that Dnmt3a ment and Dnmt3a and Dnmt3b are responsible for the and Dnmt3b played key roles in methylation of these fetal methylation of these regions. These genetic variants have enhancers. Fetal enhancers were the most significantly en- not been functionally annotated in the past because these riched group among the DMRs. regions are no longer active enhancers in adult kidneys. Six2 expression shows a strong correlation with the open- Furthermore, methylation of these regions shows strong ness and methylation of fetal enhancers. Fetal enhancers are correlation with gene expression. However, because these enriched for Six2 binding. Furthermore, the increase in meth- regions are methylated in the adult kidney, the target gene ylation of Six2-bound regions is strongly correlated with the expression is limited to the fetal stage. It will be important decrease in Six2 expression. Dnmt3a and Dnmt3b play key to study how these regions contribute to kidney disease roles in methylation of Six2-bound fetal-enhancer regions. It development. seems that Six2-bound enhancers do not achieve full methyl- Here we propose a locus-specific convergence of genetic ation in the adult kidney, indicating that Six2 might act as a and epigenetic factors in kidney disease development. The in- pioneering factor, which will need to be tested in future terplay of sequence and post-translational variations could experiments. explain how genetic and environmental factors could contrib- Although we observed a failure of full silencing of devel- ute to common disease development.55 Furthermore, because opmental genes in the Dnmt3a and Dnmt3b knockout mice, environmental and nutrient availability are critical in estab- it was highly unexpected to observe minimal phenotypic lishing the epigenome, it is possible that Dnmt3a-and changes at baseline and after injury in these animals. This Dnmt3b-mediated methylation changes play roles in kidney is a key contrast to the observed methylation changes and to disease development. We found striking similarities when we the key role Dnmt3a and Dnmt3b in other progenitor com- compared methylation of the UNCX and HOXD9 regions in partments. Dnmt3a is essential for hematopoietic stem cell healthy human and mouse kidneys. However, methylation of (HSC) differentiation because Dnmt3a-null HSCs show a diseased human kidney samples was more similar to Dntm3a marked decline in differentiation capacity over serial trans- and Dnmt3b knockout kidneys, raising the interesting possi- plantation, resulting in accumulation of undifferentiated bility that human kidney disease–specificepigeneticchanges HSCs in the bone marrow.50 Moreover, mutations in are already established during fetal development. This hypoth- DNMT3A are prevalent in myeloid malignancies51–53 and esis was raised by Barker et al.56 in the past, however it has lymphoid leukemias,54 consistent with its important func- never been conclusively proven that these changes are medi- tion in hematopoiesis. The role of Dnmt3a and Dnmt3b ated by epigenetic factors. seems to be more pronounced in rapidly proliferating In summary, we established the critical role of Dnmt3a and stem cells such as HSCs, where methylation loss over time Dnmt3b in mouse kidney development. Dnmt3a and Dnmt3b is associated with leukemia development. However, it is play critical roles in de novo methylation and decommission- worth noting that not all animals develop malignancy and ing of fetal-enhancer regions. Interestingly, most of their effect even those that develop disease will do so relatively later in in mice is observed in the postnatal period when the most life. These results indicate that cells exhibit significant plas- significant change in methylation occurs and is associated ticity in their enhancer methylation level. In addition, it with the decline in Six2 expression. Fetal enhancers methyl- seems that enhancer methylation is not critical for cell- ated by Dnmt3a and Dnmt3b appear to harbor key kidney fate stabilization. This notion will need further experimen- disease risk loci, potentially indicating their key roles in kidney tal evidence. Furthermore, although Dnmt3a/3b knockout disease development and the locus-specific convergence of ge- mice did not show alterations in a CKD model, it showed netic and epigenetic factors.

association between SNPs and kidney function. PhastCons conservation scores among 46 vertebrate species. Whole-genome bisulfite methylation patterns were shown in control and diabetic kidney disease (DKD) samples (note the lower methylation in DKD samples). (C and D) IGV genome browser view of the mouse Hoxd and human HOXD loci.

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ACKNOWLEDGMENTS DNA methylation in the human genome. Nat Genet 39: 457–466, 2007 This study was conceived of and led by Dr. Susztak, Dr. Liu, and Dr. 2. Okano M, Bell DW, Haber DA, Li E: DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian Guan. Dr. Guan performed all animal and cell experiments. Dr. Liu development. Cell 99: 247–257, 1999 performed all computational analysis. Ms. Ma, Dr. Li, Dr. Park, and 3. Gifford WD, Pfaff SL, Macfarlan TS: Transposable elements as genetic Dr. Sheng helped with animal care and data analysis. Dr. Liu, Dr. regulatory substrates in early development. Trends Cell Biol 23: Guan, and Dr. Susztak wrote the paper. 218–226, 2013 4. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al.; International Human Genome Sequencing Consortium: Initial se- DISCLOSURES quencing and analysis of the human genome [published correction appears in Nature 412: 565, 2001]. Nature 409: 860–921, 2001 5. Li SY, Park J, Guan Y, Chung K, Shrestha R, Palmer MB, et al.: DNMT1 in The Susztak laboratory is supported by Bayer, Boehringer Ingelheim, Cel- Six2 progenitor cells is essential for transposable element silencing and gene, Gilead, GSK, Lilly, Merck, ONO Pharma, and Regeneron for work that is kidney development. J Am Soc Nephrol 30: 594–609, 2019 not related to this manuscript. 6. Beckerman P, Ko YA, Susztak K: Epigenetics: A new way to look at kidney diseases. Nephrol Dial Transplant 29: 1821–1827, 2014 7. Kobayashi A, Valerius MT, Mugford JW, Carroll TJ, Self M, Oliver G, fi FUNDING et al.: Six2 de nes and regulates a multipotent self-renewing nephron progenitor population throughout mammalian kidney development. Cell Stem Cell 3: 169–181, 2008 Work in the Susztak laboratory is supported by National Institutes 8. Wanner N, Vornweg J, Combes A, Wilson S, Plappert J, Rafflenbeul G, of Health, National Institute of Diabetes and Digestive and Kidney et al.: DNA methyltransferase 1 controls nephron progenitor cell re- Diseases grants R01 DK076077, R01 DK087635, and DP3 DK108220. newal and differentiation. JAmSocNephrol30: 63–78, 2019 Dr. Park is supported by American Diabetes Association training grant 9. Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, et al.; Lifelines #1-17-PDF-036. Cohort Study; V. A. Million Veteran Program: A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51: 957–972, 2019 SUPPLEMENTAL MATERIAL 10. Qiu C, Huang S, Park J, Park Y, Ko YA, Seasock MJ, et al.: Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med 24: 1721–1731, 2018 This article contains the following supplemental material online at 11. Ko YA, Yi H, Qiu C, Huang S, Park J, Ledo N, et al.: Genetic-variation- http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2019080797/-/ driven gene-expression changes highlight genes with important func- DCSupplemental. tions for kidney disease. Am J Hum Genet 100: 940–953, 2017 Supplemental Figure 1. The spatial and temporal expression of 12. Farh KK, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, et al.: Genetic and epigenetic fine mapping of causal autoimmune disease Six2 and Cdh16 (Ksp) in the mouse kidney. variants. Nature 518: 337–343, 2015 Dnmt3a/3b Supplemental Figure 2. Effect of deletion in FA- 13. Gluck C, Qiu C, Han SY, Palmer M, Park J, Ko YA, et al.: Kidney cytosine induced kidney fibrosis model. methylation changes improve renal function decline estimation in pa- Supplemental Figure 3. Phenotypic characterization of Six2- tients with diabetic kidney disease. Nat Commun 10: 2461, 2019 Cre Dnmt3a/3b mice. 14. Park J, Guan Y, Sheng X, Gluck C, Seasock MJ, Hakimi AA, et al.: Functional methylome analysis of human diabetic kidney disease. JCI Supplemental Figure 4. Genomic location of DMRs observed in Dnmt3a/3b Insight 4: e128886, 2019 double knock-out. 15. Chen G, Chen H, Ren S, Xia M, Zhu J, Liu Y, et al.: Aberrant DNA Supplemental Figure 5. Cell-type specific base resolution meth- methylation of mTOR pathway genes promotes inflammatory activation of Cre ylation changes in Ksp Dnmt3a/3b mice. immune cells in diabetic kidney disease. Kidney Int 96: 409–420, 2019 Supplemental Figure 6. IGV genome browser of Hypo-DMR 16. Vehaskari VM, Aviles DH, Manning J: Prenatal programming of adult – regions. hypertension in the rat. Kidney Int 59: 238 245, 2001 17. Hoppe CC, Evans RG, Moritz KM, Cullen-McEwen LA, Fitzgerald SM, Supplemental Figure 7. Dnmt3a/3b mediated methylation changes Dowling J, et al.: Combined prenatal and postnatal protein restriction Six2 overlap with fetal enhancers and -binding. influences adult kidney structure, function, and arterial pressure. Am Supplemental Figure 8. Correlation between methylation changes J Physiol Regul Integr Comp Physiol 292: R462–R469, 2007 and Six2 expression. 18. Keating ST, van Diepen JA, Riksen NP, El-Osta A: Epigenetics in di- – Supplemental Figure 9. Methylation and gene expression corre- abetic nephropathy, immunity and metabolism. Diabetologia 61: 6 20, 2018 lation of topologically defined enhancer DMRs and their target genes. 19. Kato M, Natarajan R: Epigenetics and epigenomics in diabetic kidney Supplemental Figure 10. Species conservation of kidney enhancers disease and metabolic memory. Nat Rev Nephrol 15: 327–345, 2019 and their epigenetic changes in development and kidney disease. 20. Susztak K: Understanding the epigenetic syntax for the genetic al- phabet in the kidney. J Am Soc Nephrol 25: 10–17, 2014 21. Krueger F, Andrews SR: Bismark: A flexible aligner and methylation REFERENCES caller for Bisulfite-Seq applications. Bioinformatics 27: 1571–1572, 2011 22. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, 1. Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S, Rebhan M, et al.: Melnick A, et al.: methylKit: A comprehensive R package for the analysis Distribution, silencing potential and evolutionary impact of promoter of genome-wide DNA methylation profiles. Genome Biol 13: R87, 2012

JASN 31: ccc–ccc,2020 Role of Dnmt3a/Dnmt3b in Kidney 17 BASIC RESEARCH www.jasn.org

23. Li S, Garrett-Bakelman FE, Akalin A, Zumbo P, Levine R, To BL, et al.: An genes and effects on kidney-specific disease aetiologies. Nat Commun optimized algorithm for detecting and annotating regional differential 10: 29, 2019 methylation. BMC Bioinformatics 14[Suppl 5]: S10, 2013 41. Hellwege JN, Velez Edwards DR, Giri A, Qiu C, Park J, Torstenson ES, 24. Liu H, Liu X, Zhang S, Lv J, Li S, Shang S, et al.: Systematic identification et al.: Mapping eGFR loci to the renal transcriptome and phenome in and annotation of human methylation marks based on bisulfite se- the VA Million Veteran Program. Nat Commun 10: 3842, 2019 quencing methylomes reveals distinct roles of cell type-specifichypo- 42. Ernst J, Kellis M: Chromatin-state discovery and genome annotation methylation in the regulation of cell identity genes. Nucleic Acids Res with ChromHMM. Nat Protoc 12: 2478–2492, 2017 44: 75–94, 2016 43. Self M, Lagutin OV, Bowling B, Hendrix J, Cai Y, Dressler GR, et al.: Six2 25. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al.: is required for suppression of nephrogenesis and progenitor renewal in STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29: 15–21, 2013 the developing kidney. EMBO J 25: 5214–5228, 2006 26. Li B, Dewey CN: RSEM: Accurate transcript quantification from RNA-Seq 44. Bouchard M, Souabni A, Mandler M, Neubüser A, Busslinger M: data with or without a reference genome. BMC Bioinformatics 12: 323, 2011 Nephric lineage specification by Pax2 and Pax8. Genes Dev 16: 27. Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, 2958–2970, 2002 et al.: GENCODE reference annotation for the human and mouse ge- 45. Mayran A, Khetchoumian K, Hariri F, Pastinen T, Gauthier Y, Balsalobre – nomes. Nucleic Acids Res 47: D766 D773, 2019 A, et al.: Pioneer factor Pax7 deploys a stable enhancer repertoire for 28. Robinson MD, McCarthy DJ, Smyth GK: edgeR: A Bioconductor specification of cell fate. Nat Genet 50: 259–269, 2018 package for differential expression analysis of digital gene expression 46. Heuberger J, Birchmeier W: Interplay of cadherin-mediated cell ad- – data. Bioinformatics 26: 139 140, 2010 hesion and canonical Wnt signaling. Cold Spring Harb Perspect Biol 2: 29. Huang W, Sherman BT, Lempicki RA: Systematic and integrative anal- a002915, 2010 ysis of large gene lists using DAVID bioinformatics resources. Nat 47. Edeling M, Ragi G, Huang S, Pavenstädt H, Susztak K: Developmental – Protoc 4: 44 57, 2009 signalling pathways in renal fibrosis: The roles of Notch, Wnt and 30. Weng MP, Liao BY: modPhEA: Model organism Phenotype Enrichment Hedgehog. Nat Rev Nephrol 12: 426–439, 2016 Analysis of eukaryotic gene sets. Bioinformatics 33: 3505–3507, 2017 48. Zhang L, Ettou S, Khalid M, Taglienti M, Jain D, Jung YL, et al.: EED, a 31. Quinlan AR, Hall IM: BEDTools: A flexible suite of utilities for comparing member of the polycomb group, is required for nephron differentiation genomic features. Bioinformatics 26: 841–842, 2010 and the maintenance of nephron progenitor cells. Development 145: 32. Bogu GK, Vizán P, Stanton LW, Beato M, Di Croce L, Marti-Renom MA: dev157149, 2018 Chromatin and RNA maps reveal regulatory long noncoding RNAs in 49. Hon GC, Rajagopal N, Shen Y, McCleary DF, Yue F, Dang MD, et al.: mouse. Mol Cell Biol 36: 809–819, 2015 Epigenetic memory at embryonic enhancers identified in DNA meth- 33. Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T: deepTools: A ylation maps from adult mouse tissues. Nat Genet 45: 1198–1206, 2013 flexible platform for exploring deep-sequencing data. Nucleic Acids 50. Challen GA, Sun D, Jeong M, Luo M, Jelinek J, Berg JS, et al.: Dnmt3a is Res 42: W187–W191, 2014 essential for hematopoietic stem cell differentiation. Nat Genet 44: 34. Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch 23–31, 2011 JB, et al.: A single-cell atlas of in vivo mammalian chromatin accessi- 51. Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, et al.: bility. Cell 174: 1309–1324.e18, 2018 DNMT3A mutations in acute myeloid leukemia. NEnglJMed363: 35. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al.: Simple – combinations of lineage-determining transcription factors prime cis- 2424 2433, 2010 regulatory elements required for macrophage and B cell identities. 52. Walter MJ, Ding L, Shen D, Shao J, Grillot M, McLellan M, et al.: Re- Mol Cell 38: 576–589, 2010 current DNMT3A mutations in patients with myelodysplastic syn- – 36. McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al.: dromes. Leukemia 25: 1153 1158, 2011 GREAT improves functional interpretation of cis-regulatory regions. 53. Yan XJ, Xu J, Gu ZH, Pan CM, Lu G, Shen Y, et al.: Exome sequencing fi Nat Biotechnol 28: 495–501, 2010 identi es somatic mutations of DNA methyltransferase gene DNMT3A – 37. O’Brien LL, Guo Q, Lee Y, Tran T, Benazet JD, Whitney PH, et al.: Dif- in acute monocytic leukemia. Nat Genet 43: 309 315, 2011 ferential regulation of mouse and human nephron progenitors by the 54. Grossmann V, Haferlach C, Weissmann S, Roller A, Schindela S, fi six family of transcriptional regulators. Development 143: 595–608, Poetzinger F, et al.: The molecular pro le of adult T-cell acute lym- 2016 phoblastic leukemia: Mutations in RUNX1 and DNMT3A are associated 38. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, with poor prognosis in T-ALL. Genes Chromosomes Cancer 52: Malangone C, et al.: The NHGRI-EBI GWAS Catalog of published 410–422, 2013 genome-wide association studies, targeted arrays and summary sta- 55. Chamberlain AA, Lin M, Lister RL, Maslov AA, Wang Y, Suzuki M, et al.: tistics 2019. Nucleic Acids Res 47: D1005–D1012, 2019 DNA methylation is developmentally regulated for genes essential for 39. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, cardiogenesis. JAmHeartAssoc3: e000976, 2014 et al.: The human genome browser at UCSC. Genome Res 12: 56. Barker DJ, Hales CN, Fall CH, Osmond C, Phipps K, Clark PM: Type 2 996–1006, 2002 (non-insulin-dependent) diabetes mellitus, hypertension and hyper- 40. Morris AP, Le TH, Wu H, Akbarov A, van der Most PJ, Hemani G, et al.: lipidaemia (syndrome X): Relation to reduced fetal growth. Dia- Trans-ethnic kidney function association study reveals putative causal betologia 36: 62–67, 1993

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