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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

BackgroundCytosine 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 establishingde novomethylation.

MethodsWe generated mice with genetic deletion ofDnmt3aandDnmt3bin nephron progenitor cells

(Six2CreDnmt3a/3b) and kidney tubule cells (KspCreDnmt3a/3b). We characterizedKspCreDnmt3a/3bmice

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.

ResultsKspCreDnmt3a/3bmice showed no obvious morphologic and functional alterations at baseline.

Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid–inducedfibrosis. Whole-genome bisulfite sequencing indicated thatDnmt3aandDnmt3bplay 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 ofDnmt3aandDnmt3bresulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated byDnmt3a andDnmt3bwere enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice withDnmt3aandDnmt3bdeletion.

ConclusionsOur results indicate a potential locus-specific convergence of genetic, epigenetic, and

de-velopmental elements in kidney disease development.

JASN31:ccc–ccc, 2020. doi: https://doi.org/10.1681/ASN.2019080797

Cytosine methylation is erased and reestablished between generations. DNA methylation is removed from the zygote by the blastocyst stage and

rein-stated during embryonic development.1De novo

meth-yltransferases 3a (Dnmt3a) and 3b (Dnmt3b) play key

roles in establishing new methylation patterns.2

Dnmt3a-deficient animals die several weeks after birth andDnmt3b-deficient animals diein utero, indicating

the essential roles ofDnmt3a- andDnmt3b-mediated

de novomethylation in development.

Cytosine methylation has several important functions. Most cytosines in the genome are

meth-ylated for efficient silencing of transposable

ele-ments.3 Transposable elements are the footprint

of ancient integrated retroviruses, making up close

to 50% of the genome.4Recent studies indicated that

Dnmt1deletion inSix2-positive nephron progenitors

Received August 10, 2019. Accepted December 24, 2019. Y.G. and H.L. contributed equally to this work.

Published online ahead of print. Publication date available at www.jasn.org.

Correspondence: Dr. Katalin Susztak, Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, Perelman School of Medicine, University of Pennsyl-vania, 12-123 Smilow Translational Research Center, 3400 Civic Center Boulevard, Philadelphia, PA 19104. Email: ksusztak@ pennmedicine.upenn.edu

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resulted in a release of transposable-element silencing, endoge-nous retroviral expression, cytokine release, and a downstream

severe kidney developmental defect.5

Cytosine methylation is believed to be a key regulator of

gene expression.6Gene regulatory regions, such as promoters

and enhancers, contain cytosine-guanine (CpG)–rich regions

(islands). In general, unmethylated promoters are permissive to transcription-factor binding and are associated with active gene expression. Methylated promoters exclude transcription factors, therefore they are associated with gene repression. Methylation of promoter and enhancer regions plays a key role in stabilizing linage decisions and restricting lineage fates. In addition to promoters, enhancers are critical for

establish-ing cell type–specific gene regulation and gene expression.

Enhancers are enriched for cell type–specific transcription

factor binding sites to ensure cell-specific gene regulation.

Cell type–specific genes often have multiple enhancers that

loop around and join promoters to establish a cell type—specific

gene expression pattern.Six2is a critical transcription factor in

kidney development.Six2-positive progenitors can undergo a

symmetric and asymmetric division to renew or to commit and differentiate into specialized nephron epithelium

seg-ments.7,8 The role of cytosine methylation in this process is

poorly understood.

Kidney disease is a complex gene environmental disease, affecting 800-million people worldwide. Genome-wide asso-ciation analyses have been conducted to understand the her-itability of kidney function, which uncovered close to 300 loci

associated with disease risk.9–11Each nucleotide variation only

increases disease risk by a minuscule amount, however, they should explain close to 50% of disease risk in aggregate. It has been proposed that human disease-associated genetic variants

are enriched on cell type–specific enhancer regions.12

Nucleotide-sequence changes at enhancer regions could alter transcription-factor binding, leading to quantitative differ-ences in gene expression contributing to disease development. Upon analyzing kidney disease risk loci, we found that only

20%–30% of identified loci are located in regions annotated as

enhancers in adult kidney samples. The underlying mecha-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

be specific for rare kidney cell types or a disease or

develop-mental stage that is not captured by bulk analysis of adult human kidney tissue samples.

Environmental and nutritional alterations play equally

im-portant roles in kidney disease development.13–15Intrauterine

nutrient availability is known to be an important determinant of hypertension and kidney disease development, so called

“prenatal programming.”16,17Because epigenome-editing

en-zymes need substrates from the intermediate metabolism, it has been proposed that the epigenome might play a key role in prenatal programming. Nutrient availability, such as the

pres-ence of diabetes, remains the most significant risk factor for

kidney disease development.18 Indeed, the effect of poor

glycemic control on diabetic kidney disease development can be observed even decades after improved metabolic

control.19,20 It also remains unclear how environmental

and genetic factors interact and lead to kidney disease development.

To understand the role ofde novomethylation in kidney cell

differentiation, we generated mice with genetic deletion of

Dnmt3a andDnmt3b in nephron progenitor cells (NPCs)

and tubule cells, usingSix2CreandKspCre(Six2CreDnmt3a/3b

andKspCreDnmt3a/3b), respectively. Whole-genome bisulfite

sequencing (WGBS) and reduced representation bisulfite

se-quencing (RRBS) identified significant changes in the

meth-ylome of kidney tubule cells. We showed that Dnmt3aand

Dnmt3bplay important roles inde novomethylation of fetal

enhancers that were initially bound bySix2. The decline in

Six2expression during development was associated with a loss

of H3K27ac and an increase in methylation. These fetal

en-hancers decommissioned byDnmt3aand Dnmt3bwere

en-riched for kidney disease risk loci. Diseased kidney samples

showed a methylation pattern that was similar to theDnmt3a/

3bknockout animals. Overall, our data suggest that changes

brought on byDnmt3a/3b might be important for human

kidney disease.

METHODS

Animal Strains

Mice were raised and maintained in a barrier facility. Exper-iments were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Penn-sylvania and were performed in accordance with the

institu-tional guidelines. For folic acid–induced nephropathy mouse

models, 8-week-old male mice were injected with folic acid (250 mg/kg, dissolved in 300 mM sodium bicarbonate) intra-peritoneally and euthanized on day 7. For the cisplatin-induced injury model, 8-week-old male mice were injected with cisplatin (25 mg/kg) intraperitoneally and euthanized on day 3.

Significance Statement

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Real-Time RT-PCR

RNA was isolated from mouse kidney using Trizol (Invitro-gen) and was reverse transcribed using the cDNA Archival Kit (Life Technologies). Real-time RT-PCR was performed using the SYBR Green Master Mix (Applied Biosystems). Primer pair sequences are shown in (Supplemental Table 1).

BUN and Creatinine Level

Serum creatinine was measured using Creatinine Enzymatic

and Creatinine Standard (Pointe Scientific). Serum BUN was

measured using Infinity Urea Liquid Stable Reagent (Thermo

Scientific). Both measurements were performed according to

the manufacturers’instructions.

Staining

Kidneys were harvested from mice, rinsed in PBS,fixed in 10%

formalin, and embedded in paraffin. Tissue sections were

stained with hematoxylin and eosin.

Isolation of CDH161Cells

Kidneys were harvested from control and KspCreDnmt3a/3b

double knockout mice and minced using a razor blade. About

0.25 g tissue was digested in 1.17 ml RPMI plus 50ml Enzyme

D, 25 ml Enzyme R, and 6.75ml Enzyme A from the Multi

Tissue Dissociation Kit 1 (Miltenyi Biotec) and incubated for 10 minutes at 37°C. Kidney was then dissociated using 21-gauge and 26.5-21-gauge needles and incubated for 10 minutes at 37°C. The dissociation step was repeated twice. To

neutral-ize the enzymes, 10% serum was added. Cells were thenfi

l-tered through the 70-mm nylon mesh to isolate single cells and

they were then centrifuged at 1000 rpm for 5 minutes. The pellet was treated with red blood cell lysis buffer and washed with PBS. The cell suspension was incubated with CDH16 antibody (Santa Cruz). CDH16-positive cells were magneti-cally isolated using anti-mouse IgG microbeads (Miltenyi

Biotec) following the manufacturer’s instruction.

RRBS

Genomic DNA from whole kidney was isolated using the DNeasy Kit (Qiagen). Libraries were generated from the iso-lated DNA using the Premium Reduced Representation

Bisul-fite Sequencing Kit (Diagenode) following the manufacturer’s

instruction. The 2100 Bioanalyzer (Agilent) and High Sensi-tivity DNA Kit (Agilent) was used for quality check. After trimming adapter and low-quality reads using Trim Galore version 0.5.0 (https://github.com/FelixKrueger/TrimGalore)

with the option“–rrbs,”Bismark version 0.19.121was applied

to align reads to the mouse genome (mm10). MethylKit

ver-sion 1.8.122was used to quantify the methylation level of CpG

sites covered by at leastfive reads and to calculate the

meth-ylation difference betweenDnmt3a/3b and control samples.

CpG sites with methylation difference .20% and qvalue

,0.01 were analyzed in edmr version 0.6.4.123to identify

dif-ferentially methylation regions (DMRs) with at least three

CpG sites and methylation difference.20%.

WGBS

Genomic DNA was isolated fromCdh16-positive cells using

the MagAttract HMW DNA Kit (Qiagen) according to the

manufacturer’s instruction. The concentration of DNA was

measured using the Quant-iT PicoGreen dsDNA Assay kit

(Life Technologies) following the manufacturer’s instruction.

DNA quality was checked on agarose gel. After trimming adapter and low-quality reads by Trim Galore, Bismark was used for alignment to the mouse genome (mm10),

dedupli-cation, and quantification of methylation level for each CpG

site. SMART version 2.2.824was used to performde novo

ge-nome segmentation with default thresholds. The fragments

with at least five CpG sites and methylation difference

.20% were identified as DMRs.

RNA Sequencing

Total RNA from whole kidneys were isolated using the RNeasy Mini Kit (Qiagen). RNA quantity and quality was analyzed on the 2100 Bioanalyzer (Agilent) using the RNA 6000 Pico Kit (Agilent). Sequencing reads were aligned to the mouse

ge-nome using STAR version 2.2.125and gene expression was

quantified using RSEM version 1.3.1.26Comprehensive gene

annotation (gencode.vM18.annotation.gtf ) was obtained

from GENCODE.27 Differentially expressed genes (DEGs)

were identified using edgeR version 3.24.3,28with the

thresh-olds of false discovery rate,0.001 and log2 fold change.1.

Functional enrichment of DEGs were performed using DAVID

database version 6.829and the modPhEA database.30

Functional Annotation of DMRs

We downloaded mouse kidney reference epigenomes,

includ-ing WGBS and histone modifications (embryonic day 14.5

[E14.5], E15.5, postnatal day 0 [P0], and 8 weeks old) from the ENCODE website (Y. He, M. Harihran, D. U. Gorkin, D. E.

Dickel, C. Luo, R. G. Castanon,et al., unpublished

observa-tions; D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee, B. Li,

Y. Zhao,et al., unpublished observations). To define

regula-tory elements, mouse kidney chromatin states (E14.5, E15.5, E16.5, and P0) were downloaded from http://enhancer.sdsc. edu/enhancer_export/ENCODE/chromHMM/replicated/. These chromatin states were estimated by ChromHMM (D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee, B. Li, Y. Zhao,

et al., unpublished observations) using a 15-state model.

ChromHMM uses a combinatorial pattern of histone modifi

-cations (H3K4me1, H3K4me2, H3K4me3, H3K27ac, H3K27me3, H3K9ac, H3K9me3, and H3K36me3). The

15-state model was further simplified into four states including

promoter, enhancer, transcription, and other. Chromatin states from multiple data sets were merged. BEDTools version

2.27.031was used to intersect chromatin states and DMRs.

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Con KspCreDnmt3a/3b

20x

60x

Relative expression

3d 10d 21d 56d Proximal tubule

* *** **

Slc34a1 20

15

10

5

0

3d 10d 21d 56d Loop of Henle

Slc12a1

10 8 6 4 2 0 12

3d 10d 21d 56d Distal tubule

Slc12a3 6

4

2

0

3d 10d 21d 56d Collecting duct

Aqp2

3

1

0 4

2

Control

KspCreDnmt3a/3b

0.04

0.03

0.02

0.01

0.00

P=0.0008

P=0.082

Dnmt3a Dnmt3b

Relative expression

Control

KspCreDnmt3a/3b

0.5

0.4

0.3

0.2

0.1

0.0

Creatinine (mg/dL)

Con

Ksp Cre

Dnmt3a/3b

KspCreDnmt3a/3b+Veh KspCreDnmt3a/3b+Cisplatin

Con+Veh Con+Cisplatin

BUN(mg/dL)

P=0.012 P<0.0001

0 50 100 150 200

0 20 40 60

80 P<0.0001 P=0.037 Cd68

0 10

5 15 20

P=0.027 P=0.0002

Ccl2

Relative expression

P=0.020P=0.065

0 20 10 30 40 50 2500

1500 2000

500 1000

Kim1

P=0.012 P=0.005

0 2000 4000 6000 8000

100 200 300 400 500

Lcn2

Relative expression

Slc12a3

3

1 4

2

0

Relative expression

0.0 0.5 1.0 1.5

Slc12a1 P=0.001

Relative expression

0.0 0.5 1.0 1.5

Slc34a1

P<0.0001

KspCreDnmt3a/3b+Veh

KspCreDnmt3a/3b+Cisplatin Con+Veh

Con+Cisplatin

A

D

E

B

C

F

H

G

Figure 1. Phenotypic characterization ofKspCreDnmt3a/Dnmt3bmice. (A) Breeding scheme for generatingKspCreDnmt3a/3bdouble

knockout mice (KspCreDKO). (B) Transcript levels ofDnmt3aandDnmt3bin kidneys of 3-day-old control andKspCreDnmt3a/3bmice.

Data are represented as mean6SEM;Pvalue was calculated by two-tailedttest. (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 andKspCreDnmt3a/3bmice. Scale bar: upper 20mm; bottom: 10mm. (E) Relative mRNA levels of

kidney segment markersSlc34a1,Slc12a1,Slc12a3, andAqp2in control andKspCreDnmt3a/3bmice 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 withpost hocTukey test. (F) Serum BUN levels in

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ChromHMM using the 15-state model using the combinato-rial patterns of chromatin immunoprecipitation followed by

high-throughput sequencing (ChIP-seq) profiles (H3K4me1,

H3K4me3, H3K36me3, H3K27me3, H3K27ac, CTCF, and RNA polymerase II) obtained from the mouse ENCODE

proj-ect.32Similarly, the 15-state model was simplied into four

states such as promoter, enhancer, transcription, and other. Chromatin accessibility of DMRs in kidneys of 8-week-old

male C57BL/6J mice were quantified by deepTools version

3.1.233 using bigwigles obtained from the Mouse

sci-ATAC-seq Atlas.34 Transcription factor motif enrichment

analysis of DMRs was performed usingfindMotifsGenome.pl

of HOMER version 4.10.3.35GREAT version 3.0.036was used

to predict functions ofcis-regulatory regions.

Six2Binding Sites in NPCs

Six2binding sites (ChIP-seq peaks) in NPCs were downloaded

from GUDMAP database (RID:Q-Y4CY).37Six2peaks

iden-tified in at least two of the three replicates were used for further

analysis. To build a control set, shuffled regions, matching in

size and number, were generated using BEDTools shuffle.

Topologically Associating, Domain-Constrained Map of Enhancer DMR-DEG Associations

We obtained a topologically associating domain-constrained map of enhancer-promoter associations from a reference (D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee, B. Li, Y. Zhao,

et al., unpublished observations). BEDTools was used for in-tersect analysis. The Spearman correlation test was performed to examine the relationship between enhancer DMR (eDMR) methylation and DEG expression.

Enrichment of Genome-Wide Association Study Single-Nucleotide Polymorphisms in Developmental

DMRs andDnmt3aandDnmt3bDouble Knockout

DMRs

Genome-wide association study (GWAS) single-nucleotide polymorphisms (SNPs) were obtained from the GWAS catalog

(gwas_catalog_v1.0-associations_e96_r2019–06–20.tsv).38

Afterfiltering out SNPs for missing coordinates and signifi

-cance (P,531028), 79,744 SNPs were used for follow-up

analysis. The University of California Santa Cruz (UCSC) Ge-nome Browser LiftOver function was used to lift over the

mouse coordinates into human coordinates,39resulting in a

final set of 36,045 GWAS SNPs. The hypergeometric test was

used to determine the significance of disease trait and

enrich-ment of DMRs.

SNPs showing a significant association with eGFR were

downloaded from recent publications.9,40,41 The signicant

(P,531028) SNPs from different studies were combined

(26,637). The human SNP coordinates were converted to mouse genome (mm10) using LiftOver from the UCSC

Ge-nome Browser,39resulting in anal set of 7923 eGFR SNPs.

Quantification and Statistical Analysis

Statistical analyses were performed using R or GraphPad Prism software (GraphPad Software, La Jolla, CA).

Two-tailed t test or Wilcoxon signed rank sum test was used to

compare two groups. One-way ANOVA was used to compare multiple groups. Spearman rank correlation was used to de-termine the correlation. When needed, multiple testing cor-rection was performed using the false discovery rate.

Data Availability

All sequencing data (WGBS, RRBS, and RNA sequencing) have been deposited in the National Center for Biotechnology

Information’s Gene Expression Omnibus (GEO) and are

ac-cessible through GEO accession number GSE134267.

RESULTS

Deletion ofde NovoMethyltransferasesDnmt3aand

Dnmt3bin Renal Progenitors

To understand the role ofde novomethyltransferases in kidney

development and maturation, we generated double knockout

transgenic mice by crossing the Dnmt3af/f mice and the

Dnmt3bf/f mice with KspCre mice (KspCreDnmt3a/3b or

KspCreDKO) (Figure 1A). KspCre mice express Cre

recombi-nase under the control of the mouse cadherin 16.Chd16or

Ksp-cadherin is expressed from E11.5 in the developing kid-ney (Supplemental Figure 1A). Its expression increases as the

kidney matures (Supplemental Figure 1B). Cell type–specific

expression and open chromatin data indicated that it is ex-pressed in distal tubules, collecting duct, loop of Henle, and proximal tubules in adult kidney (Supplemental Figure 1, C

and D). KspCreDnmt3a/3bmice were born at the expected

Mendelian ratio. The genetic deletion was confirmed by

quan-titative RT-PCR in kidneys of 3-day-old mice (Figure 1B).

Transcript levels of Dnmt3a and Dnmt3b were lower in

KspCreDnmt3a/3bmice (Figure 1B); however, as reported

ear-lier, the expression ofDnmt3bwas around the detection limit

at birth (Figure 1B).

KspCreDnmt3a/3b mice showed no obvious renal pheno-typic alterations at baseline. Serum creatinine level of

KspCreDnmt3a/3b was comparable to littermate controls (Figure 1C). Kidney structural analysis showed no observable abnormalities (Figure 1D). To further understand the role of

one-way ANOVA. (G) Relative mRNA level of AKI markers (Kim1andLcn2) and cytokines (Cd68andCcl2). Data are represented as

mean6SEM;Pvalue was calculated by one-way ANOVA. (H) Relative mRNA level of kidney segment markers such asSlc34a1,Slc12a1,

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Dnmt3aandDnmt3bin renal development, we quantified the

expression of renal segment–specific markers (Figure 1E).

Slc34a1 (proximal tubule),Slc12a1(loop of Henle), Slc12a3

(distal tubule), andAqp2(collecting duct) were quantified on

day 3, 10, 21, and 56. Gene expression levels showed minor alterations in the developing and maturing kidneys, however

they were overall similar between adultKspCreDnmt3a/3bmice

and littermate controls (Figure 1E).

Ksp

Cre

Hypo-DMRs

Six2

Cre

Hypo-DMRs

E

Control Control

KspCre DKOSix2Cre

DKO

3-week-old

Kidney

Reduced representation bisulfite sequencing

(RRBS)

Loss of methylation in Dnmt3a/3b

Both n=196

Methylation difference (Six2Cre DKO – Control) 7,184 Six2Cre DKO DMRs

including 20,953 DMCs

Loss 6,777 94.3%

Gain 407 5.7%

Loss 1,039 71.3%

Gain 418 28.7%

Loss 1,546 30.9%

Gain 3,464 69.1% 1,457 KspCre DKO DMRs

including 3,045 DMCs

5,010 Developmental DMRs including 13,194 DMCs

-log10 (q value)

Methylation difference (KspCre DKO – Control)

Methylation difference (P21 – P0) -1.0 -0.5 0 0.5 1.0 -1.0 -0.5 0 0.5 1.0

-1.0 100

75

50

25

0

-0.5 0 0.5 1.0

Fraction of Hypo-DMRs

10%

36%

6%

48%

27% 12% 32% 29% 100%

80%

60%

40%

20%

0%

Hyper DMCs

D

Hypo DMCs No DMCs No CpGs detected Developmental DMC

A

C

Six2CreOnly

Hypo-DMRs n=6,581 KspCreOnly Hypo-DMRs

n=843

#

* *

& *

* *

* *

DNA methylation

0.0 0.25 0.5 0.75 1.0 Both Hypo-DMRs

n=196

Fetal Control (Week 3 )

Ksp

Cre Dnmt3

a/b

Six 2

Cre Dnmt3a/b Control

(Week 3) Fetal

Six2Cre

DKO KspCre

DKO

E14.5 E15.5 E16.5 P0

Mean methylation

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

B

KspCre Only n=843

Six2Cre

Only n=6,581

Figure 2. Dnmt3aandDnmt3bplay 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). Thexaxis shows mean methylation differences and theyaxis shows statistical significance (as

2log10[qvalue]). (C) The overlap between DMRs identified inSixCreDnmt3a/3b, KspCreDnmt3a/3band during kidney

develop-ment (P0–P21). (D) Overlap of hypo-DMRs identified inKspCreDnmt3a/3bandSixCreDnmt3a/3b. (E) Methylation changes during

mouse kidney development (E14.4, E15.5, E16.5, and P0), followed by methylations inKspCreDnmt3a/3b,SixCreDnmt3a/3band

littermate controls (left). Mean methylation levels from zero (blue) to one (red); mean methylation levels of DMRs in fetal, littermate

controls,KspCreDnmt3a/3bandSixCreDnmt3a/3bkidneys (right). Mean methylation difference between control kidney and fetal,

KspCreDnmt3a/3b and SixCreDnmt3a/3b kidney were compared by Wilcoxon signed rank sum test. #P53.831026, &P50.84,

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Mouse phenotype

Hypo-DMRs

Hyper-DMRs

Abn. kidney cortex morphology

Abn. oogenesis

Abn. nerve conduction

Enhanced behavioral response to alcohol

Decreased nerve conduction velocity

Polyphagia

-Log10(FDR)

0 10 20 30

Abn. renal corpuscle morphology

Abn. renal glomerulus morphology

Abn. urinary system development

Abn. kidney development

Chromatin states

Others Transcript Enhancer Promoter

Fraction of DMRs/non-DMRs

overlapped with chromatin states

Hypo-DMRHyper-DMRNon-DMRHypo-DMRHyper-DMRNon-DMR Fetal

Kidney states

Adult Kidney

states

100%

80%

60%

40%

20%

0%

All CpGs Fetal Promoter Adult Promoter Fetal Enhancer Adult Enhancer

Global CpG methylation

0.0 0.2 0.4 0.6 0.8 1.0

E14.5

ESC E15.5 E16.5 P0 Week 3 Week 8

DKO

D

E

G

38%

16% 18% 10% 7% 13%

E14.5

ESC E15.5E16.5P0AdultControlDKO E15.5P0 W8 E15.5P0 W8 E15.5P0 W8 PT_1 PT_2 PT_3 PT_5 PT_S3DCT DCT_CDCD LOH_2LOH_3GlomerularPodocytes

Hypo-DMRs (5,686) overlapped with kidney enhancers

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

Fetal enhancer

Only (3,321, 58.4%)

Fetal & adult Enhancer

(1,864, 32.8%)

Adult enhancer

Only (501, 8.8%)

DNA

methylation RPKM

(ChIP - Input)

-5 0 5 10 15

-5k DMR

5k -5k DMR

5k -1.5k DMR

1.5k -1.5k DMR

1.5k

0.0 0.25 0.5

ATAC-seq signal (RPKM)

0.0 0.25 0.5

0.75 1.0

F

Control 3-week-old

Cdh16 antibody

MACS column

Whole genome

bisulfite sequencing

(WGBS) Kidney cell

suspension and antibody

incubation

Cdh16+

cell sorting Kidney

KspCre

Dnmt3ab DKO

A

Log2 number of CpGs in segment

10

8

6

4

2

0 Hypo-DMRs 13,276

980,525 fragments (>=5 CpGs) by SMART

Methylation difference in Cdh16+ cells (DKO - Control)

Hyper-DMRs 4,302

-1.0 -0.5 0.0 0.5 1.0

B

CpG methylation DKO Cdh16

+ cells

0.0 0.2 0.4 0.6 0.8 1.0

DMRs after Dnmt3ab DKO (n=17,578)

CpG methylation Control Cdh16+ cells

0.0 0.2 0.4 0.6 0.8 1.0

C

4,302

13,276

Figure 3. Base-resolution methylome analysis of isolated tubule cells in control andKspCreDnmt3a/3bmice. (A) Experimental design.

(B) Volcano plot,xaxis shows differentially methylated regions (20% change in at leastfive CpG of any fragment) andyaxis shows

the number of DMRs. (C) Methylation level of 17,578 DMRs inCdh161cells in control (yaxis) andKspCreDnmt3a/3b(xaxis). Color

(8)

Next, we analyzed whether KspCreDnmt3a/3b mice show alterations in response to injury. To model AKI, we treated

control and KspCreDnmt3a/3b mice with cisplatin. Serum

BUN level of cisplatin-treated mice was significantly

in-creased, confirming the injury. Serum BUN level was lower

in KspCreDnmt3a/3b mice when compared with cisplatin-treated controls (Figure 1F). Tubule injury markers such as

kidney injury molecule 1 (Kim-1) and Lipocalin-2 (Lcn2) were

lower in KspCreDnmt3a/3b mice compared with controls

(Figure 1G). The macrophage marker Cd68and cytokine

Ccl2were higher in cisplatin-treated animals but they were

less prominent inKspCreDnmt3a/3bmice (Figure 1G). Kidney

segment–specific marker genes, such as the proximal tubule

marker Slc34a1 and loop of Henle marker Slc12a1 were

decreased after cisplatin treatment and these changes were

comparable inKspCreDnmt3a/3bmice (Figure 1H). We also

analyzed the injury response in the folic acid–induced kidney

fibrosis model. We did not observe significant differences

be-tween control and KspCreDnmt3a/3b mice in the folic

acid–induced kidneyfibrosis model (Supplemental Figure 2).

BecauseChd16expression is segment specific and expresses

at later stages of development, we next genetically deleted

Dnmt3aandDnmt3bin nephron progenitors using theSix2Cre

mice (Six2CreDnmt3a/3b or Six2CreDKO) (Supplemental

Figure 3A).Six2is expressed at E11.5, and it labels the

self-renewing nephron progenitors that give rise to all nephron

epithelia5(Supplemental Figure 1, A, B, and D).Six2CreDnmt3a/

3bmice were born at the expected Mendelian ratio. Kidney

sec-tions of 3-week-oldSix2CreDnmt3a/3bmice showed no obvious

structural abnormalities (Supplemental Figure 3B). Transcript

levels of kidney segment markers (Slc34a1,Slc12a1,Slc12a3, and

Aqp2) in 3-week-oldSix2CreDnmt3a/3bmice were comparable

to littermate controls (Supplemental Figure 3C). Taken together,

Dnmt3aandDnmt3bappeared dispensable in adult kidney tu-bule cells at baseline. However, they seem to confer some

resis-tance to AKI, but not to kidneyfibrosis.

Dnmt3aandDnmt3bPlay Important Roles in

Establishing Methylation Patterns during Kidney Development

To examine the contribution ofDnmt3aandDnmt3bto

kid-ney cytosine methylation changes during development, we

performed RRBS on whole-kidney samples obtained from

3-week-old control andKspCreDnmt3a/3band Six2CreDnmt3a/

3b mice (Figure 2A). On average, RRBS quantified

methyl-ation levels of approximately 2.4-million CpG sites represent-ing approximately 10.8% of CpG sites in the mouse genome.

Using stringent criteria (of qvalue,0.01 and methylation

difference .20%), we identified 7184 DMRs in kidneys of

Six2CreDnmt3a/3b mice and 1457 DMRs in kidneys of

KspCreDnmt3a/3bmice (Figure 2B). As expected, most regions

showed lower methylation levels inDnmt3a/3bknockout mice

(94.3% of the DMRs in Six2CreDnmt3a/3b and 71.3% in

KspCreDnmt3a/3bmice) (Figure 2B), indicating thatDnmt3a

andDnmt3bplay a key role inde novomethylation. Regions

that failed to gain methylation in the Dnmt3ab/3b double

knockout kidneys were mostly fetal kidney enhancers (Supplemental Figure 4).

Next we examined global methylation patterns at birth (P0) and at 3 weeks of age (P21) (Figure 2B), which is the time when nephron development ceases in mice. As reported

ear-lier, there was a significant gain in CpG sites methylation

(69.1%) during this period (P0–P21). This is consistent with

the terminal differentiation of cells during this period.

Develop-mental DMRs failed to gain methylation inDnmt3a/3b

knock-out mice (chi-squared testP51.05310233; Figure 2C). This

effect was more pronounced in theSix2CreDnmt3a/3banimals

compared with KspCreDnmt3a/3b mice. There was a limited

overlap between hypo-DMRs observed inSix2CreDnmt3a/3b

and KspCreDnmt3a/3b mice (Figure 2, D and E), indicating

that the targets ofDnmt3aandDnmt3bare spatially and

tem-porally different. In summary, cytosine methylation was severely

reduced in kidneys of mice lackingde novomethyltransferases

such asSix2CreDnmt3a/3bandKspCreDnmt3a/3b.

Dnmt3aandDnmt3bAre Necessary forde Novo

Methylation of Fetal-Specific Enhancers

For accurate cell type–specific, base-resolution methylome

analysis, we performed WGBS on sortedCdh16

(Ksp-cadherin)-positive cells (Figure 3A). Cells were isolated from kidneys of

3-week-oldKspCreDnmt3a/3band control mice. The methylation

patterns of Cdh16-positive cells showed strong concordance

with whole-kidney methylation (Supplemental Figure 5A). To

identify DMRs, wefirst segmented the genome into 980,525

KspCreDnmt3a/3bbased on GREAT for functional annotation. (E) The overlap between hypo-DMRs and hyper-DMRs inKspCreDnmt3a/

3bmice 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 inDnmt3a/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, andKspCreDnmt3a/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 itsflanking 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 inDnmt3a/3bknockout mice. Abn., abnormal; CD, collecting duct; DCT, distal tubule; DKO, double knockout; ESC,

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Fetal

A

Motif Hoxc9 Six2 PBX2 Six1 Dlx3 NF1 HNF1b EWS:ERG PU.1 Etv2

Gain methylation in adult (23,640)

Lose methylation in adult (23,293) Type Homeobox 1.00E-287 1.00E-188 1.00E-136 1.00E-132 1.00E-131 1.00E-181 1.00E-102 1.00E-43 1.00E-26 1.00E-24 Homeobox Homeobox Homeobox Homeobox CTF ETS ETS ETS Homeobox P Consensus Developmental DMRs Adult Hoxc9 Six2 Dlx3 Lhx1 CDX4 PBX2 Six1 Lhx3 Lhx2 Pdx1 Homeobox Homeobox Homeobox Homeobox Homeobox Homeobox Homeobox Homeobox Homeobox Homeobox

Motif Type Consensus P

DMRs gaining methylation in adult kidney and losing methylation in

DKO kidney cells (n=1,951)

1.00E-30 1.00E-25 1.00E-23 1.00E-22 1.00E-20 1.00E-19 1.00E-17 1.00E-15 1.00E-13 1.00E-13

Fetal Adult DKO

Hoxc9 Six2 PBX2 Six1 Dlx3 Gata6 EWS:ERG ETS PU.1 ELF3 Etv2

Gain methylation in DKO (4,302) Lose methylation in DKO (13,276)

Homeobox Homeobox Homeobox Homeobox Homeobox ETS Zf ETS ETS

Motif Type Consensus P

DKO DMRs 1.00E-63 1.00E-44 1.00E-34 1.00E-24 1.00E-16 1.00E-16 1.00E-10 1.00E-09 1.00E-06 1.00E-05 Control DKO

B

C

p < 2.2e-16

0.3 0.6 0.0 -0.3 -0.6 Methylation difference

(adult kidney – fetal kidney)

p < 2.2e-16

NPC Six2 peak Shuffled regions NPC Six2 peaks Shuffled regions 0.6 Methylation difference

(DKO - Control)

0.3

0.0

-0.3

-0.6

Methylation difference (adult kidney – fetal kidney) NPC Six2 peaks Shuffled regions

Percentage of segments overlapped with enhancer 100% 80% 60% 40% 20% 0% Fetal enhancer only Fetal & adult enhancer Adult enhancer only Not kidney enhancer

D

0.6 0.3 0.0 -0.3

NPC Six2 peaks

Methylation difference

(DKO - Control) 56.1%

-0.6

Shuffled regions

26.9%

-0.6 -0.3 0.0 3.0 6.0 -0.6 -0.3 0.0 3.0 6.0

F

E

G

NPC Six2 peaks Shuffled regions

ESC

E14.5 E15.5 E16.5 Week 3 Week 8

ESC

E14.5 E15.5 E16.5 Week 3 Week 8

P0 P0 1.00 0.75 0.50 0.25 0.00

Global CpG methylation

>100kb

Enhancer

2,946 bp 122,489 bp

Hypo-DMR CpG island Gene WGBS H3K27ac Six2 E15.5 P0 Adult Control DKO E15.5 P0 Adult NPC NPC NPC Promoter

NPC Six2 peaks Shuffled regions

Fetal enhancer only Adult enhancer only Fetal & adult enhancer 1.0 0.5 -0.5 -1.0 0.0

Correlation between Six2

expression and

CpG methylation

8.9e-16 1.1e-09 0.073

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 versusKspCreDnmt3a/3bkidneys. Fragments that gained

methylation during development but failed to gain methylation inDnmt3a/3bknockout 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 versusKspCreDnmt3a/3bkidneys (left) or adult versus fetal kidneys (right) that

overlap withSix2binding versus background shuffled region. (D) Scatter plot of methylation changes ofSix2binding sites in NPCs,

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fragments, each of them containing at leastfive CpG sites, as

established in the SMART method (Supplemental Figure 5B).24

The differential methylation analysis identified 17,578 fragments

(DMRs) showing at least 20% change in methylation between

KspCreDnmt3a/3bmice and controls (Figure 3B, Supplemental

Table 2). Consistently,.75% (13,276) of DMRs showed a lower

methylation level (hypo-DMRs) in kidneys ofKspCreDnmt3a/3b

mice. Loci with intermediate (40%–80%) methylation level were

mostly affected in theKspCreDnmt3a/3bmice (Figure 3C).

Func-tional annotation of these DMRs indicated that demethylation events tended to be close to genes associated with morphogen-esis and kidney development (Figure 3D).

To define regions that are specifically altered byDnmt3a

-and Dnmt3b-mediated differential methylation, we mapped

DMRs to functional regulatory elements. Kidney-specific

functional regulatory elements such as enhancers and pro-moters were annotated by integrating multiple histone ChIP

data obtained from fetal and adult mouse kidney samples.42

We found thatDnmt3a- andDnmt3b-mediated DMRs were

enriched on enhancers, but hardly ever observed on promoter regions (Figure 3E, Supplemental Figure 5C). When we com-pared fetal and adult kidneys, we found that loci with lower

methylation (hypo-DMRs) inDnmt3a/3bknockout mice were

strongly enriched on fetal-enhancer regions (38% versus 13%,

chi-squared testP,2.2310216), and to lesser degree on

re-gions identified as transcribed regions in fetal kidneys

(Figure 3E).

Next, we wanted to understand the fate of the fetal en-hancers and the role of methylation. Upon analyzing cytosine methylation on a genome-wide scale during kidney develop-ment, we found that adult promoters and enhancers showed a mild, gradual decline in global methylation level, indicating their openness was determined earlier (Figure 3G). On the

other hand, fetal enhancers and promoters gained significant

methylation during the P0–P21 time period. Kidneys of

Dnmt3a/3bknockout mice failed to gain methylation,

consis-tent with their role asde novomethyltransferases (Figure 3G).

To further explore the function ofDnmt3a/3bin kidney

development, we identified 3334 DMRs that showed

methyl-ation changes during development and also showed changes inDnmt3a/3bknockout mice. Most (58.5%) of these shared

DMRs underwentde novo methylation in development but

failed to increase their methylation inDnmt3a/3bknockout

mice, and were enriched in fetal-enhancer regions (Supplemental Figures 5, C and D, and 6). When focused our analysis on the hypo-DMRs that overlapped with kidney

enhancers, we found that 58% were fetal specific whereas only

a small fraction (8.8%) of hypo-DMRs were annotated as en-hancers only in the adult mouse kidney (Figure 3F, Supplemental Figure 5C). Hypo-DMRs gained methylation, losing enhancer marks and chromatin accessibility in the adult kidney (Figure 3F, Supplemental Figure 5, E and F). These

results revealed Dnmt3aand Dnmt3b were necessary forde

novo methylation and decommissioning of fetal-specific

enhancers.

Dnmt3aandDnmt3bAre Required for

Decommissioning of Fetal Enhancers Bound by Developmental Transcription Factors

Because DMRs were enriched on developmental-enhancer re-gions, we were interested to understand whether we could identify critical transcription factors associated with these sites. We used transcription factor motif analysis established

by HOMER35to analyze DMRs from the WGBS data set. Upon

comparing regions that gained methylation during develop-ment, we found a measurable enrichment for homeobox

tran-scription factors, includingSix2which is known to play a key

role in kidney development (Figure 4A). Through examining

DMRs identified in kidneys of KspCreDnmt3a/3b mice, we

again found enrichment for homeobox transcription factors,

such asSix2binding sites (Figure 4A). The overlap between

the developmental DMRs and Dnmt3a/3bknockout DMRs

showed significant enrichment for kidney developmental

transcription factor binding sites including Hoxc9, Six2,

Six1, andDlx3(Figure 4A).Six2, a kidney developmental

tran-scription factor, is required for nephron progenitor

mainte-nance.7,43These results indicate that a good portion of fetal

enhancers that are decommissioned byDnmt3a/3bare bound

bySix2.

Because motif analysis cannot distinguish among closely

related transcription factors, we next specifically examined

Six2 binding sites identified in NPCs by ChIP-seq.37 The

Six2peaks in NPCs were affected by methylation because

they were enriched for fetal hypo-DMRs (Figure 4B)

com-pared with a shuffled background. More than half (56%) of

the fragments that overlapped with NPCSix2 peaks gained

methylation in development but failed to be methylated in

KspCreDnmt3a/3b mice (Figure 4, C and D, Supplemental Figure 7A). These fragments were localized to genes that are

known to play roles in kidney development. For example,Six2

peaks in nephron progenitors overlapped with the distal

en-hancer of Pax2 which is a critical regulator of kidney

methylation in adult kidneys, but not inKspCreDnmt3a/3bmice. (E) IGV genome browser view of thePax2region (chromosome 19:

44729363–44851852). DNA methylation changes in hypo-DMRs (lower methylation inKspCreDnmt3a/3b), note the methylation pattern

in the developing mouse kidney (E15.5, P0), wild-type and Dnmt3a/3b knockout (DKO) mice, H3K27ac enhancer mark, andSix2

binding. Note the failure to increase in methylation of a fetal-enhancer region. (F) Global CpG methylation in NPCSix2 peaks and

shuffled regions at different stages of kidney development. (G) Correlation between gene expression ofSix2and 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 NPCSix2peaks and shuffled regions, andPvalues was provided for each comparison. ESC,

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B

D

A

C

F

E

18

15

12

-log10(FDR)

9

6

3

0

-10 -5

Log2 transformed fold change

0 5 10

Down-regulated (27) Up-regulated (122)

3-week-old Kidney

RNA sequencing

(RNA-seq)

eDMR-DEG associations

8

6

4

2

0

2

-4

-0.4 0.4

Pdia2

Tiam1

Spearman’s rho

Elf3

Methylation difference (DKO - Control)

Expression difference Log2 (DKD/Wild type)

0

31 eDMR-DEG associations (31 eDMRs ~ 21 DEGs) Spearman’s rho = -0.4, p = 0.02

0.8

0.6

0.4

0.2 24

16

8

0

Tiam1 expression

(TPM)

CpG Methylation

E14.5 E15.5 E16.5

W

eek 3 Control Week 3 DKO

P0

Spearman’s rho = -0.84 p-value = 0.0027 DKO Hypo-DMR

Gene

E15.5

WGBS

RNA-seq Chromatin state

P0

P0 Adult

Adult Control

Control

Control DKO

DKO

DKO

chr16:89705992-90036540 chr16:89943409-89945895

E15.5

Biological processes

Mouse phenotype

Enhancer

Promoter DMR

Positive

Negative

0.2 0.4 0.6 0.8

Tiam1 chr16:89944403-89944892 Epithelial cell differentiation

Programmed cell death

Collecting duct development Kidney development Wound healing Alpha-amino acid catabolic process Cellular amino acid catabolic process Reponse to wounding Anion transport

Epithelium development Up

Down

-Log10(p value)

0 2 4 6

Renal/urinary system phenotype Abnormal renal/urinary system physiology Abnormal urine osmolality Abnormal renal/urinary system morphology Abnormal kidney physiology Abnormal thrombosis Heart left ventricle hypertrophy Decreased t cell proliferation Homeostasis/metabolism phenotype Abnormal blood homeostasis

Up

Down

TAD-constrained map of enhancer-promoter associations Control

KspCre DKO

Figure 5. Dnmt3a- andDnmt3b-mediated methylation represses developmental genes in late development stage. (A) Schematics of

the experiments. (B) Gene expression changes inKspCreDnmt3a/3bmice. Volcano plot;xaxis shows fold-change difference, andyaxis

shows statistical difference (2log false discovery rate [2logFDR]). Red dots represent genes with higher expression inKspCreDnmt3a/

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

(12)

development.44This enhancer showed low methylation levels

in the fetal kidney and its methylation level increased in the adult kidney (Figure 4E, Supplemental Figure 7A). In contrast to the wild-type mice, this enhancer failed to gain methylation

in kidneys of theKspCreDnmt3a/3bmice and the regions

re-mained similar to those in fetal kidneys. Furthermore, chro-matin conformation capture contact matrices revealed

interactions between this enhancer and the Pax2 locus

(Supplemental Figure 7B). Integrative analysis revealed that

Six2binding sites had lower methylation levels in fetal kidneys

and gained methylation after birth (Figure 4F), indicating an

interaction between Six2 binding and methylation changes.

For example, pioneering transcription factors not only play roles in opening closed chromatin sites during development, but their binding footprint can be observed even after the

transcription factor is no longer expressed.45To explore this

hypothesis, we calculated the correlation of fragment

methyl-ation andSix2 expression during kidney development. The

Six2-bound fragment methylation showed negative

correla-tion with Six2expression, which was particularly obvious

for fetal enhancers (Figure 4G). For example, methylation of

a locus on chromosome 7 was significantly negatively

corre-lated (Spearmanr520.97,Pvalue50.0002) with the

expres-sion ofSix2 (Supplemental Figure 8). This region included

Six2binding sites that failed to gain methylation inDnmt3a/

3bknockout mice. These results indicated that DNA

methyl-ation, mediated by Dnmt3a and Dnmt3b, preferentially

affected enhancer regions bound bySix2during kidney

devel-opment. The methylation of these sites correlated withSix2

expression.

Transcriptional Changes Observed inKspCreDnmt3a/

3bMice

Next, we performed unbiased gene expression analysis by

RNA sequencing of kidneys of control andKspCreDnmt3a/3b

mice (Figure 5A). We identified 149 genes that passed the

significance threshold for differential expression (DEGs).

Consistent with the demethylation events, most (82%, 122/ 149) DEGs showed an increase in their expression in

KspCreDnmt3a/3bmice, including several kidney

developmen-tal genes such as Wnt4and Wnt9b (Figure 5B). Functional

annotation indicated that DEGs were enriched for kidney de-velopment and epithelial differentiation functions (Figure 5C).

Next, we tested whether enhancer methylation correlates with gene expression changes. To identify targets of enhancer DMR (eDMR), we obtained topologically associating, domain-constrained maps for accurate enhancer-promoter

associations (D. U. Gorkin, I. Barozzi, Y. Zhang, A. Y. Lee,

B. Li, Y. Zhao,et al., unpublished observations). We

identi-fied 31 associations between 31 eDMRs and 21 DEGs

(eDMR~DEG). The methylation changes in these eDMRs were associated with changes in their target gene expression (Figure 5D, Supplemental Figure 9A, Supplemental Table 3). Most of the eDMR-DEG associations (84%) were direction consistent, such as lower methylation was associated with higher expression (Figure 5D). For example, an eDMR on the Tiam1 locus showed lower methylation in

KspCreDnmt3a/3bmice and an increase in transcript

expres-sion ofTiam1(Figure 5, E and F, Supplemental Figure 9B).

Tiam1 was reported to play a role in Wnt signaling and

epithelial-mesenchymal transition,46,47 and it is mostly

si-lenced in adult kidney tubules. Overall, the effect of

Dnmt3a- and Dnmt3b-mediated methylation changes on gene expression modulation was modest, and mostly af-fected genes involved in kidney development.

Dnmt3a- andDnmt3b-Mediated Methylation Changes

Are Enriched for Kidney Disease Genetic Risk Loci

GWASs have identified nucleotide variations that are

en-riched in patients with CKD. Previously, we showed that a good portion of such loci are enriched on enhancer regions

in the adult kidney,10however, more than half of GWAS loci

remain unannotated. Here, we hypothesized that kidney

disease risk loci might be specific to the developmental

stage, i.e., might be active in the fetal tissue, explaining

the lack of regulatory annotation in the adult human kid-ney. As we showed earlier, such fetal enhancers are

specif-ically methylated and decommissioned by Dnmt3a/3b in

adult kidney.

As a first step, we overlapped the entire human GWAS

catalog and DMRs identified during development. We found

variants associated with kidney function (Figure 6A) and

other kidney-associated traits were specifically localized to

kidney developmental DMRs. Next, we narrowed the DMRs

only those were found to be methylated by Dnmt3aand

Dnmt3b. These DMRs showed a strong enrichment for

kid-ney function–associated traits (Figure 6A). Functional

an-notation revealed that 68% of DMRs that overlapped with

kidney function–associated SNPs were fetal kidney–specific

enhancers, which was significantly higher than expected by

chance (chi-squared test P51.0831021 2; Figure 6B,

Supplemental Figure 10A, Supplemental Table 4). Cross-species comparison indicated these fetal kidney enhancers were conserved between mouse and human, both in se-quence and in their methylation patterns (Supplemental

in kidney development and inKspCreDnmt3a/3bmice. Transcripts per million (TPM) was used for quantication of RNA expression.

Spearman rank correlation coefficient and significance was calculated and showed. (F) IGV genome browser view of theTiam1locus

(chromosome 16: 89944403–89944892), including WGBS tracks during kidney development E15.5–P0, chromatin state and gene

expression by RNA sequencing in adult andKspCreDnmt3a/3bmice. The right panel is a zoom-in region of the eDMR. DKD, diabetic

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SNPs associated with eGFR (eGFR-SNPs)

Wuttke

13,327

553

9 17 5 4 83 65 1,418

3,510 3,124 4,934

13

311Morris Combined 26,637

eGFR-SNPs

7,923 (29.7%) eGFR-SNPs conserved in mouse

4,886 (61.7%) eGFR-SNPs overlapped with 2,901 WGBS segments Lift over to Mouse (mm10)

Overlap with WGBS segments Hellwege

Fetal enhancer

Development DMR

2,901 WGBS segments

DKO DMR

Kidney function-related traits based on GWAS

catalog

DKO DMRsDevelopment DMRs

Blood urea nitrogen levels Glomerular filtration rate (creatinine)

Glomerular filtration rate in non diabetics (creatinine) Hemoglobin

Hemoglobin levels

Idiopathic membranous nephropathy Renal function-related traits (eGRFcrea) Renal function-related traits (sCR) Serum alkaline phosphatase levels Thyroid stimulating hormone levels Vitamin D levels

-log10(p-value)

3

0 6

DMRs overlapped with GWAS SNPs associated with kidney function-related traits

Spearman’s rho = -0.58, p = 0.0018

Fetal enhancer only Fetal & adult enhancer Adult enhancer only Non enhancer

Methylation difference

in human chronic kidney disease

(DKD - Control)

Methylation difference in mouse kidney development

(Adult - Fetal) HNF1A

BCAR3 A1CF

GNAS

BCAS3

UNCX 0.2

0.1

0.0

-0.1

-0.2

-0.6 -0.3 0.0 0.3 0.6

Kidney development

Speraman’s rho = –1.0 p-value = 0.0028

0.8

0.6

0.4

0.2

0

Uncx

chr7:24389472-24389973

Six2 expression

(TPM)

CpG methylation

120

100

80

60

40

20

0 140

E14.5 E15.5 E16.5 P0

Week 3 Week 8

Fraction of enhancers

overlapped with eGFR-SNPs

900

750

600

450

300

150

0 Neither K4me1 K27ac K27ac&K4me1

49%

23%

E15.5 Week 8

Fraction of DMRs

overlapped with eGFR-SNPs

200

150

100

50

0 Other Transcript Enhancer Promoter

51% 34%

Fetal Adult

Fraction of enhancers overlapped with GWAS SNPs associated with kidney function-related traits

50

40

30

20

10

0 Neither K4me1 K27ac K27ac&K4me1

59%

28%

E15.5 Week 8

Fraction of DMRs overlapped with GWAS SNPs associated

with kidney function-related traits

Other Transcript Enhancer Promoter

39% 68%

Fetal Adult 25 30

15

10

5

0 20

B

G

H

C

E

F

A

D

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

(14)

Figure 10, B and C). Specifically, we integrated the different enhancer marks (H3K27ac and H3K4me1) in fetal (E15.5)

and adult (week 8) kidneys, and identified 51 GWAS loci that

overlapped with kidney enhancers (Supplemental Table 5). Most of these enhancers (59%) were positive for both en-hancer marks (H3K27ac and H3K4me1) in the fetal stage, but only 28% of them remained positive for both marks in

the adult kidney (chi-squared test P50.0052) (Figure 6C,

Supplemental Figure 10A). To confirm the GWAS

cata-log–based finding (which only reports the top associated

SNPs), we combined a comprehensive list of 26,637 SNPs

that were significantly associated with eGFR (eGFR-SNPs)

in the most recent GWAS studies9,40,41 (Figure 6D). More

than a half (51%) of DMRs overlapped with eGFR-SNPs were localized to fetal enhancers, and 49% of enhancers that overlapped with eGFR-SNPs were enriched for enhancer marks (H3K27ac and K3K4me1) in fetal kidneys (Figure 6, E and F). These results raise the possibility that genetic variants

in fetal enhancers decommissioned byDnmt3aandDnmt3b

contribute to human kidney disease development.

To further understand the clinical significance of ourfi

nd-ings, we analyzed the methylation of DMRs that overlapped

with GWAS SNPs associated with kidney function–related

traits in microdissected kidney tubule samples obtained from healthy subjects and patients with diabetic kidney

dis-ease.14Methylation changes during kidney development were

significantly negatively correlated with methylation changes

observed in diabetic kidney disease (Figure 6, G and H), sug-gesting the methylation patterns established during develop-ment were either reversed in diabetic kidney disease or failed to establish during development.

For example, GWASs have revealed that nucleotide

vari-ants nearbyUNCXwere significantly associated with kidney

functions (Supplemental Figure 10D). The methylation pat-tern of this locus in healthy human kidneys was similar to the mouse kidney, including a large area of lowly methylated

region (Figure 7, A and B). Kidney function–associated

GWAS variants were localized to a fetal enhancer which showed the active enhancer marks H3K27ac and H3k4me1 in fetal kidneys. Although the region remained minimally H3K4me1 positive in the adult kidney, this region was no longer positive for H3K27ac, indicating that it was not an active enhancer in adult kidneys. This region showed an in-crease in methylation level during development and failed to

gain methylation in absence ofDnmt3aandDnmt3b,

indi-cating the key role ofDnmt3aandDnmt3bin establishing

the methylation of this GWAS region. Consistent with the

notion that this region is a fetal-specific active enhancer,

Uncx/UNCXwas expressed in fetal but not in adult kidney (Figures 6H and 7A, Supplemental Figure 10E), indicating

the important role of Dnmt3aand Dnmt3bin

decommis-sioning fetal enhancers. Finally, when compared with healthy kidney samples, kidney tubules from patients with diabetic kidney disease showed strong similarities to kidneys of Dnmt3a/3bknockout mice, such as the loss of cytosine methylation of this region (Figure 7B). In addition to the

UNCX/Uncx locus, we also examined the Hoxd/HOXD

locus. Again, we found a similar pattern, such as

conserva-tion between the human and mouse locus, Dnmt3a/3b

-mediated methylation, and decommissioning of fetal enhancers (Figure 7, C and D, Supplemental Figure 10F).

In summar y, our results indicate that Dnmt3a- and

Dnmt3b-mediated methylation of fetal enhancers are en-riched on kidney disease risk loci.

DISCUSSION

Here,viaintegrating mouse genetic studies and genome-wide

methylome and expression profiling, we elucidated the role of

Dnmt3aand Dnmt3bin renal tubule epithelium in develop-ment, maturation, adult, and diseased mouse kidneys. Using

base-resolution temporal profiling, we described dynamic

changes of DNA methylation during kidney development. Globally, we observed the largest decline in global methylation level between embryonic stem cells and renal progenitors, whereas the greatest increase in methylation was observed

during postnatal maturation (P0–P21). In mice, during the

first 3 weeks, new nephrons are formed, epithelial cells

pro-liferate, and the kidney enlarges drastically.48Regions that act

as enhancers in the adult kidney, show very small and gradual changes in their methylation level during development. Changes in methylation, on the other hand, are associated with alterations in transcript expression that again occur in the postnatal stage. These results indicate that the epigenetic state of these regions is likely established early during kidney development and their postnatal expression is mostly tran-scriptionally controlled.

Our results indicated that kidney-specific fetal enhancers

underwent important changes during postnatal kidney devel-opment. We observed a substantial increase in enhancer

methylation after birth (P0–P21). Histone-modification data

(D) Integration of eGFR-associated SNPs (eGFR-SNPs) from recently published GWAS.9,40,41The 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 ofUncxand 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

(15)

WGBS Six2 E15.5 P0 Adult Control DKO H3K27ac chr5:139515562-139580379 (mm10) E15.5 P0 Adult E15.5 P0 Adult E14.5 E15.5 E16.5 P0 Adult RNA-seq H3K4me1

A

Gene CpG island

Renal disease SNPs

eGFR-SNPs

4930500l23Rik

Renal_function-related_traits_(BUN)

rs1123164 [0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 20]

[0 - 20]

[0 - 20]

[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]

rs76210971 rs6951209 rs4724805 rs7785293 rs4724817 rs73670555

Hemoglobin Uncx

CpG:_69 CpG:_124 CpG:_26

chr7:1239604-1308516 (hg19) Diabetic kidney disease Normal Kidney WGBS

B

WGBS −log10(p value) 60 50 40 30 20 10 0 Phastcons Gene CpG island Renal disease SNPs eGFR-SNPs

Renal_function-related_traits_(BUN)

[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]

UNCX

CpG:_26 CpG:_82

rs1123164 rs6951209 rs7785293 rs4724828

rs62435145 r2 0.8 0.6 0.4 0.2 CpG:_923

C

chr2:74593010-74824038 (mm10)

WGBS Six2 E15.5 P0 Adult Control DKO Gene CpG island

Renal disease SNPs

eGFR-SNPs H3K27ac E15.5 P0 Adult E15.5 P0 Adult E14.5 E15.5 E16.5 P0 Adult RNA-seq H3K4me1 rs72919076

rs72914763 rs72923424 rs72923454 rs72927167 rs72916126 rs72916158

Evx2

Estimated_glomerular_filtration_rate

[0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 1.00]

[0 - 20]

[0 - 20]

[0 - 20]

[1.000 - 20]

[1.000 - 20]

[1.000 - 20]

[0 - 20]

[0 - 20]

[0 - 20]

[0 - 10.00]

[0 - 10.00]

[0 - 10.00]

[0 - 10.00]

[0 - 10.00]

Hoxd12 Hoxd9 Hoxd3 Haglr

CpG:_84 CpG:_27 CpG:_99 CpG:_35 CpG:_84

chr2:176872325-177132115 (hg19)

D

−log10(p value) 20 15 10 5 Diabetic kidney disease Normal Kidney WGBS WGBS 0 Phastcons Gene CpG island Renal disease SNPs eGFR-SNPs

[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]

6253455 rs72919076 rs72923454 rs72927180 rs57225327

Estimated_glomerular_filtration_rate_(eGFR)_ Evx2

rs187355703

HOXD10 HOXD3

CpG:_77 CpG:_63 CpG:_25 CpG:_141

r2 0.8 0.6 0.4 0.2

Figure 7. Genetic and epigenetic features ofUNCXandHOXDhuman kidney disease associated loci. (A) IGV genome browser of the

mouseUncxlocus (chromosome 5 [chr5]: 139515562–139580379). The top panel shows GWAS and kidney function (eGFR)–associated

variants followed by methylation patterns (WGBS) in the developing kidney andDnmt3a/3bknockout kidney,Six2binding, H3K27ac

and H3K4me1 (enhancer marks), and gene expression by RNA sequencing (RNA-seq). (Note the failure of methylation of the enhancer

region in absence ofDnmt3a/3b). (B) IGV genome browser view of the humanUNCXlocus (chr7: 1239604–1308516). Lift-over from the

(16)

indicated that the change in methylation (at birth) was

asso-ciated with a loss of H3K27ac, a histone mark that defines

active enhancers.Dnmt3aandDnmt3bplay a critical role in

methylation of these fetal enhancers and we identified

thou-sands of enhancer regions whose methylation was not

estab-lished in absence ofDnmt3aand Dnmt3b. These enhancers

show intermediate methylation levels at the fetal stage. Al-though their methylation increases to the adult stage, they do not seem to gain full methylation in the normal adult mouse kidney. Previously, these loci have also been

called“vestigial enhancers.”49Here we showed thatDnmt3a

andDnmt3b played key roles in methylation of these fetal

enhancers. Fetal enhancers were the most significantly

en-riched group among the DMRs.

Six2expression shows a strong correlation with the

open-ness and methylation of fetal enhancers. Fetal enhancers are

enriched forSix2binding. Furthermore, the increase in

meth-ylation ofSix2-bound regions is strongly correlated with the

decrease inSix2 expression. Dnmt3a andDnmt3b play key

roles in methylation ofSix2-bound fetal-enhancer regions. It

seems thatSix2-bound enhancers do not achieve full

methyl-ation in the adult kidney, indicating thatSix2might act as a

pioneering factor, which will need to be tested in future experiments.

Although we observed a failure of full silencing of

devel-opmental genes in theDnmt3aandDnmt3bknockout mice,

it was highly unexpected to observe minimal phenotypic changes at baseline and after injury in these animals. This is a key contrast to the observed methylation changes and to

the key roleDnmt3aandDnmt3bin other progenitor

com-partments.Dnmt3ais essential for hematopoietic stem cell

(HSC) differentiation becauseDnmt3a-null HSCs show a

marked decline in differentiation capacity over serial trans-plantation, resulting in accumulation of undifferentiated

HSCs in the bone marrow.50 Moreover, mutations in

DNMT3Aare prevalent in myeloid malignancies51–53 and

lymphoid leukemias,54consistent with its important

func-tion in hematopoiesis. The role ofDnmt3a and Dnmt3b

seems to be more pronounced in rapidly proliferating stem cells such as HSCs, where methylation loss over time is associated with leukemia development. However, it is worth noting that not all animals develop malignancy and even those that develop disease will do so relatively later in

life. These results indicate that cells exhibit significant

plas-ticity in their enhancer methylation level. In addition, it seems that enhancer methylation is not critical for cell-fate stabilization. This notion will need further

experimen-tal evidence. Furthermore, althoughDnmt3a/3bknockout

mice did not show alterations in a CKD model, it showed

increased resistance to AKI, which requires rapid prolifer-ation and cell differentiprolifer-ation.

Here we show that developmental enhancers, whosede

novomethylation is specifically mediated byDnmt3aand

Dnmt3b, are enriched for kidney disease genetic risk loci. These regions are annotated as active enhancers in the fetal

kidney, many bound bySix2, a critical kidney

developmen-tal transcription factor. However, these regions are no longer annotated as active enhancers in the adult stage. Fetal enhancer methylation level increases during

develop-ment and Dnmt3a and Dnmt3b are responsible for the

methylation of these regions. These genetic variants have not been functionally annotated in the past because these regions are no longer active enhancers in adult kidneys. Furthermore, methylation of these regions shows strong correlation with gene expression. However, because these regions are methylated in the adult kidney, the target gene expression is limited to the fetal stage. It will be important to study how these regions contribute to kidney disease development.

Here we propose a locus-specific convergence of genetic

and epigenetic factors in kidney disease development. The in-terplay of sequence and post-translational variations could explain how genetic and environmental factors could

contrib-ute to common disease development.55Furthermore, because

environmental and nutrient availability are critical in

estab-lishing the epigenome, it is possible that Dnmt3a- and

Dnmt3b-mediated methylation changes play roles in kidney disease development. We found striking similarities when we

compared methylation of theUNCXandHOXD9regions in

healthy human and mouse kidneys. However, methylation of

diseased human kidney samples was more similar toDntm3a

andDnmt3bknockout kidneys, raising the interesting

possi-bility that human kidney disease–specific epigenetic changes

are already established during fetal development. This

hypoth-esis was raised by Barkeret al.56in the past, however it has

never been conclusively proven that these changes are medi-ated by epigenetic factors.

In summary, we established the critical role ofDnmt3aand

Dnmt3bin mouse kidney development.Dnmt3aandDnmt3b

play critical roles inde novomethylation and

decommission-ing of fetal-enhancer regions. Interestdecommission-ingly, most of their effect in mice is observed in the postnatal period when the most

significant change in methylation occurs and is associated

with the decline inSix2expression. Fetal enhancers

methyl-ated by Dnmt3a andDnmt3b appear to harbor key kidney

disease risk loci, potentially indicating their key roles in kidney

disease development and the locus-specific convergence of

ge-netic and epigege-netic 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

Figure

Figure 1. Phenotypic characterization of KspCreDnmt3a/Dnmt3b mice. (A) Breeding scheme for generating KspCreDnmt3a/3b doubleknockout mice (KspCreDKO)
Figure 2. Dnmt3arepresentation of RRBS on whole-kidney lysates of and Dnmt3b play important roles in establishing methylation patterns during kidney development
Figure 3. Base-resolution methylome analysis of isolated tubule cells in control and KspCreDnmt3a/3b mice
Figure 4. Ksprichment (HOMER) of DMRs, during kidney development (P0CreDnmt3a/3b DMRs are enriched for developmental transcription factor binding
+4

References

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