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  • Open Access

Methylome profiling of healthy and central precocious puberty girls

  • 1,
  • 2,
  • 3,
  • 1, 4,
  • 1,
  • 1,
  • 1,
  • 5,
  • 5,
  • 5,
  • 1,
  • 4,
  • 6,
  • 3, 7,
  • 7 and
  • 1, 8Email author
Clinical Epigenetics201810:146

https://doi.org/10.1186/s13148-018-0581-1

  • Received: 7 August 2018
  • Accepted: 7 November 2018
  • Published:

Abstract

Background

Recent studies demonstrated that changes in DNA methylation (DNAm) and inactivation of two imprinted genes (MKRN3 and DLK1) alter the onset of female puberty. We aimed to investigate the association of DNAm profiling with the timing of human puberty analyzing the genome-wide DNAm patterns of peripheral blood leukocytes from ten female patients with central precocious puberty (CPP) and 33 healthy girls (15 pre- and 18 post-pubertal). For this purpose, we performed comparisons between the groups: pre- versus post-pubertal, CPP versus pre-pubertal, and CPP versus post-pubertal.

Results

Analyzing the methylome changes associated with normal puberty, we identified 120 differentially methylated regions (DMRs) when comparing pre- and post-pubertal healthy girls. Most of these DMRs were hypermethylated in the pubertal group (99%) and located on the X chromosome (74%). Only one genomic region, containing the promoter of ZFP57, was hypomethylated in the pubertal group. ZFP57 is a transcriptional repressor required for both methylation and imprinting of multiple genomic loci. ZFP57 expression in the hypothalamus of female rhesus monkeys increased during peripubertal development, suggesting enhanced repression of downstream ZFP57 target genes. Fourteen other zinc finger (ZNF) genes were related to the hypermethylated DMRs at normal puberty. Analyzing the methylome changes associated with CPP, we demonstrated that the patients with CPP exhibited more hypermethylated CpG sites compared to both pre-pubertal (81%) and pubertal (89%) controls. Forty-eight ZNF genes were identified as having hypermethylated CpG sites in CPP.

Conclusion

Methylome profiling of girls at normal and precocious puberty revealed a widespread pattern of DNA hypermethylation, indicating that the pubertal process in humans is associated with specific changes in epigenetically driven regulatory control. Moreover, changes in methylation of several ZNF genes appear to be a distinct epigenetic modification underlying the initiation of human puberty.

Keywords

  • Human puberty
  • Central precocious puberty
  • DNA methylation
  • Epigenetics
  • Genomic imprinting
  • Zinc finger genes

Background

The onset of puberty is heralded by an increased pulsatile secretion of gonadotropin-releasing hormone (GnRH), which upon reaching the anterior pituitary activates the pituitary–gonadal axis. Epidemiological studies have provided evidence supporting a genetic influence on pubertal timing [13]. However, the age at normal puberty varies greatly among girls (8–13 years) and the genetic basis for such a variability remains largely unknown [4]. A potential underlying mechanism is the modulation of gene activity by epigenetic factors, which may be important for the broad regulation of pubertal timing [5]. In fact, it appears that up to 20% of the variance of puberty initiation involves environmental factors, such as nutrition, stress, exposure to endocrine-disrupting chemicals, and intrauterine conditions [5, 6].

Epigenetics refers to the alterations in gene expression that are not caused by changes in DNA sequence itself [7]. DNA methylation (DNAm) is one of the best studied epigenetic mechanisms involved in modulating gene activity [8, 9]. It consists of the covalent addition of a methyl (-CH3) group to the fifth position of the pyrimide base of DNA, cytosine, and occurs mostly in cytosine–phosphate–guanine (CpG) dinucleotides [8].

Epigenetics has been implicated as a regulatory system underlying GnRH secretion [10, 11]. The study of DNAm in the medial basal hypothalamus of male rhesus monkeys revealed a decrease in methylation status of the GnRH gene’s 5′ CpG island that paralleled an increase in GnRH mRNA levels across puberty [12]. Indeed, increased DNAm of gene promoters is commonly associated with gene silencing [13, 14]. Recently, silencers of the Polycomb group were identified as major drivers of an epigenetic mechanism of transcriptional repression that is lifted at the beginning of female puberty in rats, allowing the pubertal process to proceed unimpeded [10]. Importantly, manipulations of DNAm in animal models were shown to alter the onset of puberty. Thus, inhibiting DNAm resulted in pubertal failure, whereas inducing DNA hypermethylation led to earlier onset of puberty [10, 15]. DNAm also plays an essential role in genomic imprinting, an epigenetic phenomenon recently implicated in the regulation of puberty. Initial evidence for this concept came from studies showing that common variants located at the loci of three imprinted genes (MKRN3, DLK1, and KCNK9) were associated with the age at menarche in a large European women cohort [16]. More direct evidence was provided by the demonstration that central precocious puberty (CPP) due to loss-of-function mutations in the paternally expressed imprinted genes MKRN3 (makorin ring finger 3) and DLK1 (delta-like 1 homolog) is an imprinting disorder [1719].

In the present study, we used peripheral blood leukocytes to investigate the relationship that may exist between DNAm patterns and pubertal timing in healthy and CPP-affected girls. We compared pre- with post-pubertal control subjects to interrogate changes in the methylome profile that occurs during physiological pubertal development. We also compared CPP patients with healthy girls to analyze the DNAm changes in CPP.

Results

Description of the analyzed human groups

We studied ten female patients with familial CPP (index cases) who were referred for clinical and/or genetic evaluation to the Endocrinology Unit at Clinical Hospital, Sao Paulo, Brazil. CPP in girls was diagnosed based on the presence of breast Tanner stage 2 (B2) before the age of 8 years, pubertal basal and/or GnRH-stimulated LH levels, and bone age advanced more than 1 year (Greulich and Pyle atlas). Clinical and hormonal features of the patients with CPP are described in Table 1. The mean age at pubertal onset of these girls was 6.4 years (ranging from 3 to 8 years). At the time of the first evaluation (mean age of 7.7 years), Tanner B3 was observed in 50% of the girls and Tanner B4 in the remaining 50%. The mean Δ [bone age − chronological age] was 2.2 ± 1.1 years. None of the patients were obese (mean body mass index (BMI) Z-score = 0.7). Mean basal LH levels were 1.4 ± 1.4 IU/L, and mean LH levels after GnRH stimulation were 18.9 ± 14 IU/L. Mean basal FSH levels were 3.4 ± 1.7 IU/L. Median estradiol (E2) levels were 13 pg/mL. All CPP patients had normal brain magnetic resonance imaging.
Table 1

Clinical and hormonal features of the patients with CPP

Family number

Initial clinical manifestation (age, y)

Time of diagnosis

BA (y)

LH (IU/L)

FSH (IU/L), Basal

E2 (pg/mL)

Age (y)

Breast Tanner stage

BMI (Z)

Basal

After GnRH

After leuprolide depot 3.75 mg

1

Telarche (5.8)

6.7

4

1.2

11

1.9

31.5

NA

6.2

53.3

2

Telarche (7.7)

8.2

3

0.09

11

< 0.6

NA

17.6

4.2

31

3

Telarche (5.5)

8.1

3

0.17

11

1

NA

8.8

1.9

17.4

4

Telarche (7.5)

10.2

4

1.29

11

4.2

NA

NA

NA

98.8

5

Telarche (7.3)

8

3

1.28

11

1.5

NA

45.8

2.7

< 13

6

Telarche (6.6)

7.3

4

1.41

9.5

0.1

16.9

16

NA

< 13

7

Telarche (5.5)

7.1

3

0.11

7.8

< 0.6

NA

6.5

1.5

< 13

8

Telarche (3)

6.5

4

0.64

8.8

< 0.6

4.2

9.8

3.7

< 13

9

Telarche (8)

8

4

− 0.09

10

0.1

6.9

4.6

2

< 13

10

Telarche (6.9)

6.9

3

0.85

7.8

3.5

35

NA

5.2

< 10

Abbreviations: BA bone age, BMI body mass index, E2 estradiol, NA not available, y years

Familial CPP was defined by the presence of more than one affected member in a family [2]. The pedigrees of the ten families are illustrated in Fig. 1. Only female members were affected in all families. Regarding the mode of inheritance, CPP was maternally inherited in four families (pedigrees 2, 3, 6, 9), paternally inherited in four families (pedigrees 1, 7, 8, 10), and undetermined in two families (pedigrees 4 and 5). A MKRN3 inactivating mutation (p.R328H) was detected in family 10 by Sanger sequencing. Whole-genome sequencing revealed a complex defect in DLK1 ( 14 kb deletion and 269 bp duplication) in family 1 [18]. The remaining families (pedigrees 2 to 9) were previously studied by whole-exome sequencing without identifying a genetic mutation associated with CPP phenotype [17].
Fig. 1
Fig. 1

Pedigrees of the families with CPP. Squares indicate male members, circles female members, black symbols clinically affected members, and arrows the probands

The control group was composed of 33 healthy Brazilian girls. Pubertal stage was characterized by physical signs (Tanner criteria) and hormonal evaluation (Tables 2 and 3). Fifteen of these girls were at pre-pubertal stage with mean chronological age of 6.7 years, ranging from 2.6 to 9 years. All of them exhibited Tanner B1 upon physical medical evaluation and pre-pubertal basal LH levels (< 0.1 IU/L). The mean BMI Z-score was 0.2 ± 0.6. The remaining 18 girls were at pubertal stage, with mean chronological age of 13.1 years, ranging from 9.5 to 16.3 years. The majority of them (61%) had Tanner B4. In the pubertal group, mean basal LH levels were 5.2 ± 2.4 IU/L, mean basal FSH levels were 5.4 ± 1.8 IU/L, and mean E2 levels were 67.6 ± 42.7 pg/mL. The mean BMI Z-score was 0.1 ± 0.7.
Table 2

Clinical and hormonal features of the pre-pubertal control group

Pre-pubertal control number

Chronological age (years)

BMI (Z)

Breast Tanner stage

Pubic hair Tanner stage

Basal LH (IU/L)

Basal FSH (IU/L)

E2 (pg/mL)

1

6.1

0.55

1

1

< 0.1

3

< 15

2

8.1

− 0.16

1

1

< 0.1

1.4

< 15

3

7.2

− 0.73

1

1

< 0.1

0.7

< 15

4

6.6

1.12

1

1

< 0.1

3.1

< 15

5

6.2

0.61

1

1

< 0.1

1.4

< 15

6

5.2

0.63

1

1

< 0.1

1.5

< 15

7

5.7

0.99

1

1

< 0.1

2.4

< 15

8

7.2

0.44

1

1

< 0.1

1.6

< 15

9

9

− 0.02

1

1

< 0.1

2.9

< 15

10

5.4

−  0.86

1

1

< 0.1

8.9

< 15

11

8.6

0.07

1

1

< 0.1

1.8

< 15

12

7.2

− 0.47

1

1

< 0.1

2.2

< 15

13

7.9

0.90

1

1

< 0.1

2.4

< 15

14

7.5

0.32

1

1

< 0.1

3.3

15.7

15

2.6

− 0.6

1

1

< 0.1

3.5

< 15

Abbreviations: BMI body mass index, E2 estradiol

Table 3

Clinical and hormonal features of the pubertal control group

Pubertal control number

Chronological age (years)

BMI (Z)

Breast Tanner stage

Pubic hair Tanner stage

Basal LH (IU/L)

Basal FSH (IU/L)

E2 (pg/mL)

1

14.4

− 0.55

4

5

12

9.1

58

2

12.5

0.06

4

4

1.3

2.5

37.9

3

11.2

− 0.5

3

3

5.7

6.9

68.9

4

12

0.44

4

4

4.3

4.7

35.7

5

16.3

0.56

5

5

4.9

6.5

39.1

6

12.7

0.03

4

4

8.8

6.7

135.4

7

16

1.06

5

5

3.8

2.5

< 15

8

15.7

1.35

5

5

7.2

4.6

56.6

9

11.3

0.99

4

5

5.8

3.1

30.9

10

11.9

− 0.65

4

4

3.3

6.2

57.1

11

11.3

− 0.22

3

2

5.1

5.4

32.2

12

15.7

0.71

5

4

6.7

6.8

76

13

12.2

0.04

4

4

4.2

6.5

36.9

14

9.5

− 0.03

3

2

3.3

6.6

62.6

15

14.3

− 1.7

4

5

3.5

6.5

59.9

16

12.2

0.39

4

5

2.8

3

119.2

17

13.8

0.3

4

5

5.9

5.7

122.5

18

13.1

0.01

4

5

5.2

4.7

173.6

Abbreviations: BMI body mass index, E2 estradiol

Changes in DNA methylation associated with normal puberty

Comparison between pre-pubertal and pubertal healthy girls revealed the presence of 120 differentially methylated regions (DMRs) (false discovery rate (FDR) < 0.05 and methylation differences > 5%), with all but one (99%) being hypermethylated in the pubertal group (Table 4). Most of the DMRs (89 DMRs, 74%) were located on the X chromosome, none of them mapped to the pseudoautosomal regions (PAR) of this chromosome. The 120 DMRs harbored the promoter regions of 127 genes, whose functions were enriched for various biological processes, such as intracellular receptor signaling pathway, messenger RNA (mRNA) transcription, histone modification, and genetic imprinting (Additional file 1).
Table 4

Genomic regions with methylation differences > 5% between pre- and post-pubertal control groups

DMR identification

Chromosome

DMR start

DMR end

Number of CpGs

Mean beta difference (pre-pubertal minus post-pubertal values)

Promoters of genes included in the genomic region

DMR_1372

10

42,862,876

42,863,594

9

− 0.105162452

RP11-313 J2.1

DMR_8814

4

81,117,647

81,119,473

11

− 0.089040315

PRDM8

DMR_4264

16

1,583,810

1,584,516

8

− 0.084174074

IFT140, TMEM204

DMR_11738

X

48,814,205

48,815,856

16

− 0.08181042

OTUD5, RNU6-722P

DMR_9935

6

31,148,332

31,148,748

14

− 0.078236383

POU5F1

DMR_12001

X

153,671,800

153,672,898

8

− 0.076624259

FAM50A, GDI1

DMR_11933

X

132,090,860

132,092,772

7

− 0.075099028

HS6ST2

DMR_11979

X

152,066,221

152,066,735

7

− 0.073224659

NA

DMR_11993

X

153,598,742

153,600,494

7

− 0.072362363

FLNA

DMR_11972

X

149,861,526

149,862,363

7

− 0.070984952

MTMR1

DMR_9959

6

31,650,735

31,651,362

18

− 0.070185244

LY6G5C

DMR_10023

6

33,047,185

33,049,505

22

− 0.069255178

HLA-DPB1, HLA-DPA1, RPL32P1

DMR_11751

X

49,643,431

49,644,706

7

− 0.067077238

USP27X-AS1, USP27X

DMR_11804

X

71,130,538

71,131,891

7

− 0.066738144

NHSL2

DMR_11950

X

135,578,793

135,580,181

18

− 0.063932352

HTATSF1

DMR_7255

20

5,485,144

5,485,511

8

− 0.062564433

LINC00654

DMR_3109

12

130,823,570

130,824,831

8

− 0.062155177

PIWIL1

DMR_11802

X

70,712,403

70,713,213

8

− 0.0621177

INGX, Y_RNA.73, TAF1

DMR_11934

X

133,118,088

133,119,961

11

− 0.061748623

GPC3

DMR_12006

X

153,774,721

153,776,358

18

− 0.061612111

IKBKG, G6PD

DMR_5381

17

78,865,087

78,865,755

7

− 0.061329072

NA

DMR_11732

X

48,554,185

48,555,977

7

− 0.061177896

SUV39H1

DMR_11995

X

153,605,635

153,607,981

9

− 0.061083719

EMD

DMR_11921

X

129,114,238

129,118,953

19

− 0.060957678

BCORL1

DMR_11500

8

144,789,164

144,790,772

9

− 0.060451112

ZNF707, CCDC166

DMR_9283

5

77,145,356

77,147,141

8

− 0.060227285

NA

DMR_11790

X

68,723,670

68,725,815

13

− 0.06007949

FAM155B, AL158069.1

DMR_11669

X

21,391,817

21,393,898

12

− 0.059401453

CNKSR2

DMR_11823

X

79,590,789

79,591,032

8

− 0.059246208

FAM46D

DMR_11821

X

77,358,961

77,360,510

14

− 0.059121003

PGK1

DMR_11666

X

19,533,022

19,534,066

10

− 0.058953271

MAP3K15

DMR_11670

X

21,674,815

21,677,134

14

− 0.058921343

KLHL34

DMR_11678

X

24,482,846

24,484,361

10

− 0.058918654

PDK3

DMR_11711

X

46,432,770

46,434,442

9

− 0.058894854

CHST7

DMR_9843

6

28,601,271

28,601,519

11

− 0.05882722

RP11-373 N24.2

DMR_11966

X

147,581,562

147,583,550

15

− 0.058723364

AFF2, AC002368.4

DMR_11659

X

17,392,798

17,393,584

7

− 0.058571815

NHS

DMR_11900

X

119,005,413

119,006,837

14

− 0.058548975

NDUFA1, RNF113A

DMR_11742

X

48,931,388

48,932,122

11

− 0.058333756

PRAF2, WDR45

DMR_12004

X

153,718,691

153,719,406

10

− 0.058126507

SLC10A3

DMR_11999

X

153,656,860

153,657,411

7

− 0.058031895

BX936347.1, ATP6AP1

DMR_11748

X

49,056,505

49,057,013

9

− 0.058008258

SYP, SYP-AS1

DMR_2557

12

9,217,079

9,217,907

11

− 0.057945682

A2M-AS1, LINC00612

DMR_11970

X

149,529,976

149,534,258

17

− 0.057730577

MAMLD1

DMR_5006

17

37,123,638

37,124,558

10

− 0.057306128

FBXO47

DMR_6288

19

54,040,774

54,041,856

10

− 0.057304041

ZNF331

DMR_11944

X

134,232,157

134,233,109

7

− 0.057092491

LINC00087

DMR_11957

X

138,285,393

138,287,900

17

− 0.056982737

FGF13

DMR_11888

X

118,107,540

118,110,766

16

− 0.056978196

LONRF3

DMR_11764

X

54,383,906

54,385,662

7

− 0.056970851

WNK3

DMR_12003

X

153,713,930

153,715,426

10

− 0.05672695

UBL4A

DMR_11994

X

153,602,509

153,603,503

11

− 0.056715308

FLNA

DMR_11991

X

153,284,899

153,285,934

7

− 0.056647889

IRAK1, MIR718

DMR_11693

X

38,420,326

38,421,503

11

− 0.056490464

TSPAN7

DMR_11668

X

20,283,423

20,286,779

12

− 0.056376843

RPS6KA3

DMR_11672

X

21,958,416

21,959,700

10

− 0.056271754

SMS, LL0XNC01-39B3.1

DMR_1702

10

123,355,268

123,358,317

17

− 0.056225675

FGFR2

DMR_11974

X

150,150,670

150,153,136

17

− 0.056110647

HMGB3

DMR_11919

X

128,977,299

128,978,347

14

− 0.055916881

ZDHHC9

DMR_11770

X

55,187,242

55,187,903

10

− 0.055775168

FAM104B

DMR_7483

20

57,425,157

57,428,473

65

− 0.05557006

GNAS, GNAS-AS1

DMR_9132

5

23,507,030

23,507,656

7

− 0.055437765

PRDM9

DMR_11978

X

151,999,239

152,000,347

12

− 0.055139575

NSDHL, CETN2

DMR_12000

X

153,664,475

153,665,799

9

− 0.055090146

GDI1

DMR_11729

X

48,432,279

48,433,876

12

− 0.054825597

RBM3, AC115618.1

DMR_10691

7

50,849,168

50,851,503

9

− 0.054780529

GRB10

DMR_11873

X

107,334,429

107,335,227

11

− 0.054776826

PSMD10, ATG4A

DMR_11984

X

152,906,667

152,910,369

14

− 0.054741445

DUSP9

DMR_11642

X

11,774,782

11,777,794

18

− 0.054708295

MSL3

DMR_1209

1

240,656,217

240,657,329

8

− 0.054513802

NA

DMR_11894

X

118,707,913

118,709,261

14

− 0.054314233

UBE2A

DMR_11684

X

30,326,328

30,327,819

14

− 0.053883806

NR0B1

DMR_4794

17

6,898,738

6,899,888

15

− 0.053689222

ALOX12, RP11-589P10.5

DMR_11998

X

153,639,287

153,640,967

12

− 0.053584581

DNASE1L1, TAZ

DMR_11725

X

47,862,977

47,863,707

8

− 0.05357646

SPACA5, ZNF182

DMR_11648

X

15,353,254

15,354,150

12

− 0.053561291

PIGA

DMR_11911

X

122,993,419

122,994,594

14

− 0.053524318

XIAP

DMR_11743

X

48,957,691

48,958,509

12

− 0.053409715

WDR45

DMR_11741

X

48,900,948

48,901,982

9

− 0.053374186

TFE3

DMR_11915

X

128,656,725

128,657,893

11

− 0.053357781

SMARCA1

DMR_7894

22

51,016,501

51,017,151

12

− 0.053231588

CPT1B, CHKB-CPT1B, CHKB

DMR_11942

X

134,165,954

134,166,347

7

− 0.053015868

FAM127A

DMR_11720

X

47,478,591

47,479,662

11

− 0.052859687

SYN1

DMR_11777

X

62,974,433

62,975,657

10

− 0.052826713

ARHGEF9

DMR_11903

X

119,148,809

119,150,375

8

− 0.052776664

GS1-421I3.2

DMR_11918

X

128,913,578

128,913,980

7

− 0.052666423

SASH3

DMR_11923

X

129,243,062

129,246,153

12

− 0.05258674

ELF4

DMR_10704

7

56,515,510

56,516,504

10

− 0.052581782

RP13-492C18.2

DMR_11996

X

153,625,785

153,628,595

17

− 0.052516898

RPL10, SNORA70

DMR_12002

X

153,707,029

153,708,103

8

− 0.052282548

LAGE3

DMR_11892

X

118,601,891

118,603,378

9

− 0.052141155

SLC25A5, SLC25A5-AS1

DMR_11890

X

118,369,571

118,370,873

12

− 0.05200566

PGRMC1

DMR_11977

X

151,806,225

151,807,197

11

− 0.051997479

GABRQ

DMR_11781

X

64,254,409

64,255,552

7

− 0.051933195

ZC4H2

DMR_9869

6

29,629,187

29,631,447

10

− 0.051487467

NA

DMR_11852

X

101,966,398

101,967,643

8

− 0.051470117

GPRASP2

DMR_11718

X

47,341,740

47,343,198

13

− 0.051358132

ZNF41, CXorf24

DMR_11677

X

23,925,454

23,927,284

12

− 0.051200853

CXorf58, APOO

DMR_9823

6

28,058,715

28,059,208

9

− 0.051119312

ZSCAN12P1

DMR_11703

X

41,781,891

41,783,785

10

− 0.05111124

CASK

DMR_11675

X

23,760,460

23,762,372

13

− 0.051058352

ACOT9

DMR_11714

X

47,003,362

47,004,911

16

− 0.051047155

NDUFB11, RBM10

DMR_11834

X

99,661,860

99,667,528

23

− 0.051019999

PCDH19

DMR_9036

4

174,421,114

174,422,908

9

− 0.050942397

NA

DMR_11955

X

136,647,133

136,649,808

10

− 0.050903123

ZIC3, RP1-137H15.2

DMR_11909

X

119,763,444

119,764,469

11

− 0.050899975

C1GALT1C1

DMR_10849

7

100,463,206

100,465,221

11

− 0.050845841

TRIP6

DMR_4309

16

3,493,133

3,494,155

12

− 0.050709712

ZNF597, NAA60

DMR_11695

X

38,662,279

38,665,168

16

− 0.050532132

MID1IP1, MID1IP1-AS1

DMR_11785

X

67,653,156

67,653,925

10

− 0.050494689

OPHN1

DMR_12009

X

154,032,629

154,034,184

11

− 0.050494431

MPP1

DMR_11922

X

129,193,893

129,194,982

7

− 0.050454357

NA

DMR_5488

18

14,747,888

14,748,439

10

− 0.050398645

ANKRD30B

DMR_11851

X

101,905,837

101,907,254

8

− 0.050370988

GPRASP1

DMR_12011

X

154,299,274

154,300,204

8

− 0.050341015

BRCC3, MTCP1, CMC4

DMR_4813

17

7,311,030

7,312,081

9

− 0.05032058

NLGN2

DMR_11673

X

23,350,093

23,353,620

13

− 0.050150411

PTCHD1

DMR_11763

X

54,209,148

54,209,968

8

− 0.050134574

FAM120C

DMR_11902

X

119,133,852

119,135,074

7

− 0.050060465

NA

DMR_9870

6

29,648,161

29,649,084

22

0.08795856

ZFP57

Abbreviations: DMR differentially methylated region, NA not available

The single hypomethylated genomic region identified in pubertal girls encompassed the promoter region of ZFP57 (zinc finger protein 57), a KRAB domain-containing transcriptional repressor involved in both imprinting and methylation of multiple genomic loci [20, 21]. The DMR with the largest methylation difference (10.5%) contained the promoter region of RP11-313 J2.1, the zinc finger protein 91 pseudogene. In addition, the promoter regions of 13 other zinc finger (ZNF) genes (ZNF597, ZSCAN12P1, ZNF707, ZNF331, ZC4H2, ZNF182, ZNF41, ZIC3, RNF113A, ZDHHC9, PRDM8, PRDM9, RBM10) were related to these 120 DMRs.

We applied the Gene Set Enrichment Analysis (GSEA) to search for enriched transcription factors that could be targeting the identified DMRs. We detected enrichment for 20 transcription factors, and the ten most relevant are listed in Additional file 2. Importantly, one of them is the estrogen receptor (ER). Seven differentially methylated genes (SMARCA1, POU5F1, HTATSF1, ELF4, HMGB3, KLHL34, FGFR2) had ER binding sites in the region spanning up to 4 kb around their transcription start site (TSS).

Changes in DNA methylation associated with CPP

A unique DMR was detected between CPP cases and pre-pubertal controls (FDR < 0.05), and it was slightly more methylated in the CPP group (mean beta difference of 0.003391969). This genomic region (chr6: 33385679-33385786) harbored the promoter region of CUTA (homolog of Escherichia coli CutA), a gene ubiquitously expressed, including brain. Comparison between CPP cases and pubertal girls revealed the absence of DMRs (FDR < 0.05). Because of this, we explored the methylation levels at isolated CpG sites.

Comparison between CPP cases and pre-pubertal controls revealed 417 differentially methylated CpG sites (DMSs) (FDR < 0.05 and methylation differences > 10%), with the majority of them (338 DMSs, 81%) being hypermethylated in CPP patients (Fig. 2, Additional files 3 and 4). In silico functional analyses of the 199 known genes related to these 338 DMSs demonstrated enrichment for signaling pathways involved in cell communication (70 genes), regulation of response to stimuli (40 genes), and metabolism (10 genes). When comparing CPP cases with pubertal controls, we identified 605 DMSs (FDR < 0.05 and methylation differences > 10%), with the majority of them (539 DMSs, 89%) being hypermethylated in the CPP group (Fig. 3, Additional files 5 and 6). The functional characterization of 308 known genes related to these 539 DMSs revealed enrichment for metabolic pathways (13 genes), transport vesicles (10 genes), association with endocrine system diseases (10 genes), and carcinomas (13 genes). Forty-eight genes harboring hypermethylated CpG sites in CPP were ZNFs (Additional files 4 and 6).
Fig. 2
Fig. 2

Heatmap based on the methylation levels of the 417 DMSs identified between CPP cases and pre-pubertal controls. Cases are indicated in red and controls in green. Each column represents an individual, and each row represents a CpG site. Methylation levels are displayed in Z-score at the up left corner of the heatmap. Hierarchical clustering was applied to the CpG sites (rows) using Euclidian distance with average linkage

Fig. 3
Fig. 3

Heatmap based on the methylation levels of the 605 DMSs identified between CPP cases and post-pubertal controls. Cases are indicated in red and controls in green. Each column represents an individual, and each row represents a CpG site. Methylation levels are displayed in Z-score at the up left corner of the heatmap. Hierarchical clustering was applied to the CpG sites (rows) using Euclidian distance with average linkage

Normal methylation of the MKRN3 and DLK1 genes

Methylation status of the MKRN3 and DLK1 genes and their regulatory regions using two distinct methods revealed no differences between CPP patients and controls. The methylation analyses of MKRN3:TSS-DMR and MEG3/DLK1:IG-DMR (IG = intergenic) are showed in Additional file 7.

Hypothalamic expression of ZFP57 increases at puberty in nonhuman primates

To determine if hypomethylation of ZFP57 in peripheral blood leukocytes is accompanied by increased ZFP57 expression in the hypothalamus at the time of puberty, we measured ZFP57 mRNA levels in the medial basal hypothalamus (MBH) of pre- and peripubertal female rhesus monkeys. We observed that ZFP57 mRNA levels began to increase during late juvenile development and became significantly elevated at puberty, coinciding with the increase in GnRH and KISS1 expression that occurs at this time (Fig. 4a, b). To determine if a decrease in expression of ZNFs that become hypermethylated at puberty also occurs at puberty in the monkey hypothalamus, we selected five of these genes for mRNA measurement. Interestingly, expression of all five genes showed a tendency to decrease at puberty (Fig. 4b), with the change in ZNF597 being statistically significant. To further evaluate these results, we performed a correlation analysis of the changes in ZFP57 and ZNF597 expression that occurred with the advent of puberty and found the existence of a significant (p = 0.01) inverse correlation between the pubertal increase in ZFP57 mRNA levels and the decrease in ZNF597 expression (Fig. 4c). We also used the MBH of female rhesus monkeys to quantitate the hypothalamic expression of four ZNFs that become hypermethylated in CPP and found that their mRNA levels either increase at normal puberty (ZNF251) or showed no change (RNF113A, ZDBF2, and ZDHHC9) (Additional file 8a). This result is in keeping with the finding that ZNFs hypermethylated at CPP are not the same as those ZNFs that become hypermethylated at normal puberty.
Fig. 4
Fig. 4

Changes in expression of a GnRH, KISS1, and TAC3 and b selected ZNFs in the female monkey hypothalamus during pubertal development. One of the ZNFs examined (ZFP57) was hypomethylated at puberty in peripheral blood cells. The other five were hypermethylated. c Correlation analysis shows that a loss in ZNF597 expression observed at the time of monkey puberty is negatively correlated to the increase in ZFP57 mRNA levels detected at this time. The mRNA levels obtained were expressed as fold-change with regard to the values observed in the EJ group. Bars represent mean ± s.e.m. (n = 4–7/group) (*p < 0.05; vs EJ group; one-way ANOVA-SNK test). EJ early juvenile, LJ late juvenile, PUB peripubertal

In addition to defining the methylation status of MKRN3 and DLK1 in peripheral blood leukocytes of CPP patients and control subjects undergoing normal puberty, we examined the changes in MKRN3 and DLK1 mRNA levels that occur in the MBH of female monkeys at the time of puberty. No changes in expression for either gene were detected between the early juvenile and the pubertal phases of monkey puberty (Additional file 8b).

Figure 5 summarizes the main results of the present study.
Fig. 5
Fig. 5

Flow chart summarizing the main results of the present study

Discussion

In the last few years, it has become increasingly clear that epigenetic mechanisms contribute significantly to the regulation of pubertal timing. From a clinical perspective, it would be desirable to have minimally invasive methods for the identification and monitoring of at least some of these mechanisms. Here, we report the use of white blood cells to assess patterns of DNAm that occur in association with puberty in girls. Our findings reveal the existence of a broad pattern of DNA hypermethylation taking place in these cells at the time of both normal and central precocious puberty. These findings are consistent with an earlier report showing an increase in DNAm levels of peripheral blood cells during the puberty transition in girls [22].

Most of the changes we observed consisted of hypermethylation of either DMRs or DMSs, with ZNFs standing out as a population of transcriptional repressors affected by these alterations. The sole exception to this pattern was ZFP57, a transcriptional repressor required for methylation of downstream genes and imprinting of several other genes [20, 23]. Contrary to other ZNFs, ZFP57 was hypomethylated at puberty, suggesting that its expression may increase—instead of decrease—in tissues relevant to the pubertal transition. Measurement of ZFP57 expression in the hypothalamus of female rhesus monkeys undergoing puberty proved this assumption to be correct, as a significant increase in ZFP57 mRNA levels was detected at the time of puberty.

The ZFP57 gene, located at chromosome 6p22.1, encodes a protein of 516 amino acids that contains seven zinc finger motifs [21, 24]. The interaction of these motifs with specific DNA sequences in regulatory regions of target genes is required for ZNF proteins to control gene expression [24]. Importantly, in the absence of ZFP57, genomic imprinting is lost [20]. This is illustrated by the loss of differential DNA methylation at the imprinted regions Dlk1-Dio3 and Snrpn in homozygous mutant embryos (maternal-zygotic) derived from Zfp57 null female mice [20]. Notably, directly relevant to the present findings, ZFP57 has been shown to be required for normal imprinting of genomic regions controlling the expression of MKRN3 and DLK1, two genes encoding repressors of the pubertal process, with DLK1 being one of the genes most strongly affected by the absence of ZFP57 [20]. The increase of ZFP57 expression observed in the hypothalamus of pubertal female monkeys suggests that an important function of ZFP57 in the neuroendocrine brain might be to repress the activity of transcriptional repressors of puberty, such as the Polycomb complex or other ZNFs [10, 25]. Furthermore, the inverse correlation detected between the increase in ZFP57 mRNA levels and the decrease in ZNF597 expression that occurred in the hypothalamus at the time of monkey puberty suggests that ZNF597 might be one of the transcriptional inhibitors of primate puberty repressed by ZFP57. Further studies are required to test the validity of this idea.

It is now known that pubertal timing requires repression of inhibitory factors and that DNA hypermethylation of gene promoters is associated with gene silencing [11, 26, 27]. In humans, genome-wide association studies revealed associations between single-nucleotide polymorphisms located near ZNF131, ZNF462, and ZNF483 and earlier age of menarche, suggesting that ZNF genes can impact human pubertal development [16, 25, 28]. Supporting this concept are the demonstrations that MKRN3, also known as ZNF127, inhibits the human pubertal process and that MKRN3 loss-of-function mutations are the most frequent cause of familial CPP [2931]. More recently, the DLK1 gene was also proposed to play an inhibitory role in the regulation of puberty, since its deficiency was associated with a CPP phenotype in syndromic and nonsyndromic cases [18, 32]. Within this context, our results showing a broad pattern of DNA hypermethylation at puberty suggest that—if hypermethylation of ZNFs also occurs in neuroendocrine cells controlling reproductive development—ZNF expression would decrease, and downstream target genes would escape from ZNF inhibitory control at the time of puberty. A specific example of this epigenetic interaction was recently provided by the demonstration that expression of several ZNFs decreases at puberty in the hypothalamus of female nonhuman primates and that preventing this change delayed pubertal timing [25]. In the present study, we measured the mRNA levels of five of these hypermethylated genes (ZNF597, ZNF41, ZNF707, ZNF331, and PRDM8) in de hypothalamus of developing female monkeys and found that all of them showed a tendency to decrease expression at the end of pubertal development, with the changes in ZNF597 being significant.

As indicated above, there were 14 ZNFs hypermethylated at normal puberty. Intriguingly, the largest methylation difference (10.5%) was related to the zinc finger protein 91 pseudogene. At present, we do not know if it is involved in the hypothalamic control of puberty, but such a role remains possible, especially considering that 2–20% of human pseudogenes are transcribed, with some being transcribed in a tissue-specific manner maintained over the years [33]. It is therefore plausible that pseudogenes have a functional role in specific cell populations, an idea supported by the finding that noncoding RNAs produced from pseudogenes can regulate gene expression [33].

The search for transcription factors targeting the 120 DMRs associated with the pubertal process revealed an enrichment for ER. We identified seven differentially methylated genes as ER target genes, suggesting the existence of a functional relationship between them. This relationship appears to be particularly relevant to the neuronal regulation of the pubertal process, as neuronal ERα is involved in the temporal coordination of GnRH secretion, and an inhibitory ER-mediated influence on kisspeptin neurons has been shown to keep puberty in check in female mice [34]. Recently, the methylome study of 30 girls identified changes in DNAm across puberty related to estrogen-responsive genes, suggesting that differential DNAm at puberty may in part result from exposure to pubertal levels of estradiol [35].

An intriguing finding of this study was the striking prevalence of X-linked DMRs related to puberty. All the 89 X-linked DMRs were mapped outside the PAR regions of the X chromosome. PAR are short regions of homology between the mammalian X and Y chromosomes which are located at the tips of the short arm (PAR1) and long arm (PAR2) of the chromosome, and they harbor genes that escape X-inactivation [36]. Most of X-linked genes are subject to X-inactivation in females to ensure dosage compensation [36]. However, 15–20% of X-chromosomal genes escape from inactivation, and 80% of them lie on the short arm [37, 38]. Moreover, these escaping genes can have different expression levels between tissues and between females [38, 39]. Of note, some of the X-linked DMRs related to puberty identified in our study affect genes that escape X-inactivation, as MSL3, NR0B1, RBM3 e HS6ST2, and others that are heterogeneous in escape, as FGF13 e o SLC25A5 [38]. The potential contribution of genes that escape X-inactivation to the timing of puberty was previously noticed. A case in point are the reports of girls with trisomy X (47, XXX karyotype) manifesting precocious puberty [40, 41]. Early activation of the hypothalamic–pituitary–gonadal axis in these girls was attributed to the extra X chromosome and more specifically to the expression of genes that escape X-inactivation. Early puberty was also described in females with Xp.11.22-p11.23 duplication [42]. In these patients, the duplicated X is preferentially activated, probably contributing to their clinical phenotype. Our results now demonstrate that changes in methylation of genes that escape X-inactivation occur in puberty.

A Danish study identified the methylation of a region on chromosome 7, which contains the promoter of TRIP6 (thyroid hormone receptor interactor 6), to be associated with human pubertal development [43]. Our results support and extend these earlier findings by demonstrating an overlap of ten DMRs with the reported data, with seven presenting methylation changes in the same direction, including the DMR containing the TRIP6 promoter (Table 5).
Table 5

DMRs related to pubertal process in this study that were previously reported by Almstrup during transition to puberty

DMR

Chr

DMR start

DMR end

DMR width

Number of CpGs

Mean beta difference (pre-pubertal minus post-pubertal values)

Minimum FDR

Promoters of genes included in the DMR

DMR_10849*

7

100,463,206

100465221

2016

11

− 0.050845841

8.06E−11

TRIP6

DMR_10848*

7

100,449,647

100,450,634

988

8

− 0.011123245

8.92E−22

SLC12A9, RP11-126 L15.4

DMR_4835

17

7,834,607

7,835,519

913

13

− 0.004918456

2.27E−35

TRAPPC1, KCNAB3, CNTROB

DMR_5149

17

4,6687,312

46,692,859

5548

34

− 0.010869697

1.33E−51

HOXB8, HOXB7

DMR_6184

19

46,996,347

46,999,444

3098

15

− 0.017502407

1.01E−10

AC011484.1, PNMAL2

DMR_9947*

6

31,543,289

31,543,686

398

10

− 0.024464641

1.26944E−07

TNF

DMR_5887*

19

15,528,530

15,530,737

2208

18

− 0.007917542

1.19E−30

AKAP8L

DMR_1838*

11

2,925,594

2,927,247

1654

15

0.005243223

2.57836E−07

SLC22A18, SLC22A18AS

DMR_9898*

6

30,290,258

30,296,049

5792

57

− 0.014466232

3.04E−79

TRIM39, HCG18

DMR_2718*

12

52,626,427

52,627,576

1150

10

− 0.024084513

1.47E−20

KRT7

Abbreviations: Chr chromosome, DMR differentially methylated regions, FDR false discovery rate

*DMRs with methylation changes in the same direction

Our study unveils a genome-wide DNA hypermethylation in CPP, which is in accordance with animal studies [15]. To our knowledge, this is the first study describing changes in the methylome patterns of girls with CPP. Hypermethylated CpGs in 63 genes were identified in CPP patients, including 48 ZNF genes. We speculate that these genes can either contribute to CPP or represent epigenetic modifications resulting from functional changes affecting the complex genetic network underlying the CPP disease. Although we cannot distinguish between these two possibilities, we notice the absence of significant DMRs between pubertal healthy girls and CPP patients. This suggests that the main epigenetic modifications that modulate gene expression during puberty, either normal or precocious, are similar. However, this conclusion is tempered by the finding that differentially methylated genes in CPP are different from those differentially methylated in normal puberty. It might be that in CPP there are different genomic regions that become differentially methylated and that these regions remain epigenetically silent in normal puberty. In fact, only one DMR containing the promoter of CUTA was found to be more methylated in CPP patients than in pre-pubertal controls. This gene, mapped to chromosome 6p21.32, encodes a protein of 136 amino acids that plays a role in anchoring of acetylcholinesterase to neuronal membranes in the human brain [44]. The protein CUTA seems to be also involved in promoting proliferation and survival of glial cells [45]. The CUTA gene has not been implicated before in the regulation of pubertal development.

Changes in methylation of the two precocious puberty imprinted genes, MKRN3 and DLK1, could represent an interesting causal mechanism of sporadic and familial CPP. However, our results showing a normal methylation status of both genes exclude this potential mechanism as an underlying cause of CPP in our patients. It remains possible that the study of a much larger population of girls with CPP may provide evidence for such a relationship, or that the identification of novel mutations able to alter gene methylation patterns proved to be a causative factor [4649].

Conclusion

By demonstrating a widespread pattern of DNA hypermethylation associated with normal and precocious puberty in girls, our results suggest that an epigenetic mechanism involving a chemical change in DNA architecture contributes to regulating pubertal timing in humans. Because these hypermethylation patterns involve several genes, the compelling possibility emerges that the net outcome of these alterations is a modified output from networks controlling the pubertal process. The overrepresentation of ZNFs among genes affected by differential methylation and the recent demonstration of an involvement of ZNFs in the central control of female puberty in monkeys suggest that ZNFs may provide a major regulatory pathway linking DNA methylation to the control of human puberty.

Methods

Hormone assays

Serum LH, FSH, and E2 levels were measured by ultrasensitive methods, immunofluorometric assay—IFMA (AutoDELFIA, Turku, Finland), or electrochemiluminometric assay—ECLIA (Cobas e601, Roche Diagnostics, USA), with good correlation among them. The inter-assay and intra-assay coefficients of variation were 5% or less. The hormonal profile was assessed by IFMA (patients) or ECLIA (control group). For the acute GnRH stimulation test, serum LH was measured at − 15, 0, 15, 30, 45, and 60 min after i.v. administration of 100 μg of GnRH. Basal LH levels > 0.6 IU/L (IFMA) or 0.2 IU/L (ECLIA) were considered as pubertal levels, and a GnRH-stimulated LH peak > 6.9 IU/L (IFMA) or 5.0 IU/L (ECLIA) were considered as a pubertal response [50, 51]. The LH level measured 2 h after the first administration of leuprorrelin depot 3.75 mg > 10 IU/L (IFMA) or 5.0 IU/L (ECLIA) was considered pubertal [51, 52].

Sample preparation and quality control

Genomic DNA was extracted from peripheral blood leukocytes using standard procedures. DNA quality and quantity were assessed by NanoDrop (Thermo Fisher Scientific), Qubit (Thermo Fisher Scientific), and electrophoresis on 1% agarose gel. The bisulfite-converted DNA (EZ DNA Methylation kit, Zymo Research) was hybridized in the Human Methylation 450 BeadChip microarray (HM450K, Illumina), following the Illumina Infinium HD methylation protocol. We used RnBeads tools to evaluate the quality of our data, and all samples provided high-quality data [53]. Briefly, experimental quality control was performed using the microarray positive and negative control probes for staining, hybridization, extension, target removal, bisulfite conversion, specificity, and non-polymorphic sites.

Data were extracted by the iScan SQ scanner (Illumina) using GenomeStudio software (v.2011.1), with the methylation module v.1.9.0, into IDAT files.

Probes were annotated using GRCh37/hg19 coordinates from UCSC regarding genomic positions and features (FDb.InfiniumMethylation.hg19 package), with additional annotations to identify probes that exhibit multiple alignments in the genome for posterior exclusion.

Methylation levels of the CpG sites were calculated as beta values, which range continuously from 0 (unmethylated) to 1 (fully methylated) (http://www.illumina.com).

Differential methylation analyses

These analyses were performed in the R environment using Bioconductor packages (http://www.bioconductor.org).

The RnBeads package was applied to the dataset [53]. Non-specific probes (n = 28,076) were removed due to the high likelihood of cross-hybridization. Background was corrected using the Noob method, which is based on a normal-exponential convolution using out-of-band probes [54]. Normalization of signal intensities values from probes types I and II was performed using SWAN method (Additional file 9), which adjusts the intensities based on a quantile approach [55].

Technical effects and cell blood composition were corrected using default parameters from RnBeads [56]. An expected association between surrogate variables and the age at the time of blood collection was identified, but correction was not applied because these variables are related to the study design (Additional file 10). The clinical treatment for CPP with GnRH analogue did not act as a co-variable.

After the pre-processing step, 443,042 CpG sites were analyzed in pairwise comparisons (pre-pubertal versus pubertal controls, familial CPP cases versus pre-pubertal controls, and familial CPP cases versus pubertal controls).

To identify DMSs, hierarchical linear models from the limma software package followed by a fitting based on the Bayes statistics was applied to M values (log of beta values) [57]. CpG sites presenting a FDR < 0.05 and methylation differences greater than 10% were considered as the most significant and selected for further analysis.

DMRcate was applied to identify DMRs, defined as a 300 nucleotides sequence with at least seven CpG sites presenting methylation changes in the same direction [58]. Genomic regions with FDR < 0.05 and mean methylation differences greater than 5% were considered the top ones.

In silico analyses

Functional enrichment analyses were performed on the Web-based Gene Set Analysis Toolket (WebGestalt) using the whole genome as background [59]. Features with adjusted p value < 0.05 provided by the Benjamini–Hochberg multiple test were considered significant. We also used the GSEA program to search for statistically significant associations between a defined set of genes and biological states [60].

Methylation analyses of MKRN3 and DLK1 loci

Bisulfite-converted DNA samples of all patients and controls were studied using the TaqMan Allele-Specific Methylated Multiplex Real-Time Quantitative Polymerase Chain Reaction to analyze the methylation status at MKRN3:TSS-DMR and MEG3/DLK1:IG-DMR [61].

Nonhuman primates

The MBH of female rhesus monkeys (Macaca mulatta) was obtained through the Oregon National Primate Research Center (ONPRC) Tissue Distribution Program. The animals were classified into different stages of pubertal development according to the criteria proposed by Watanabe and Terasawa [62]. Early juvenile (EJ) animals were 9 months to 1.8 years of age, late juvenile (LJ) were 2–2.9 years of age, and pubertal (Pub) were 3.1–4 years old. Plasma LH levels at these ages, measured using a different set of animals (n = 10/group), were 2.59 ± 0.97 (EJ), 3.88 ± 0.92 (LJ), and 6.48 ± 1.64 (Pub) ng/ml, respectively. The MBH was dissected by making a rostral cut along the posterior border of the optic chiasm, a caudal cut immediately in front of the mammillary bodies, and two lateral cuts half-way between the medial eminence and the hypothalamic sulci, as previously reported [63]. The tissue fragments were frozen in liquid nitrogen and stored at − 80 °C until RNA extraction.

RNA extraction and quantitative (q) PCR

Total RNA was extracted from the MBH of female rhesus monkeys using the RNeasy mini kit (Qiagen, Valencia, CA). DNA contamination was removed by on-column digestion with DNAse using the Qiagen RNase-free DNase kit (Qiagen, Valencia, CA). RNA concentrations were determined by spectrophotometric trace (Nanodrop, ThermoScientific, Wilmington, DE). Total RNA (500 ng) was transcribed into cDNA in a volume of 20 μl using 4 U of Omniscript reverse transcriptase (Qiagen, Valencia, CA). mRNA was measured using the SYBR GreenER™ qPCR SuperMix system (Invitrogen, Carlsbad, CA). Amplification primers were designed using the PrimerSelect tool of DNASTAR 14 software (Madison, WI) on the NCBI online Primer-Blast program (Additional file 11). PCR reactions were performed in a volume of 10 μl (1 μl of diluted cDNA, 5 μl of SYBR GreenER™ qPCR SuperMix, and 4 μl of primers mix; 1 μM of each gene-specific primer). The PCR conditions used were 5 min at 95 °C, 40 cycles of 15 s at 95 °C, and 60 s at 60 °C. Formation of a single SYBR Green-labeled PCR amplicon was confirmed by subjecting each PCR reaction to a three-step melting curve analysis (15 s at 95 °C, 1 min at 60 °C, ramping up to 95 °C at 0.5 °C/s, detecting every 0.5 s, and ending with 15 s at 95 °C). The qPCR reactions were performed using a QuantStudio 12 K Real-Time PCR system (Thermo Fisher, Waltham, MA), and a QuantStudio 12 K Flex software (Thermo Fisher, Waltham, MA) was used to detect threshold cycles (CTs). Standard curves were constructed by serially diluting (1/2 to 1/512) a pool of cDNAs derived from a mix of equal amounts of cDNA from each sample. The mRNA content of each sample was estimated by referring the corresponding CTs to the relative standard curve, and the values obtained were normalized for procedural losses using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA as the normalizing unit.

Abbreviations

B: 

Breast Tanner stage

BMI: 

Body mass index

CpG: 

Cytosine–phosphate–guanine

CPP: 

Central precocious puberty

DMR: 

Differentially methylated region

DMS: 

Differentially methylated site

DNAm: 

DNA methylation

E2: 

Estradiol

ECLIA: 

Electrochemiluminometric assay

EJ: 

Early juvenile

ER: 

Estrogen receptor

FDR: 

False discovery rate

GnRH: 

Gonadotropin-releasing hormone

GSEA: 

Gene Set Enrichment Analysis

IFMA: 

Immunofluorometric assay

IG: 

Intergenic

LJ: 

Late juvenile

MBH: 

Medial basal hypothalamus

mRNA: 

Messenger RNA

PAR: 

Pseudoautosomal region

Pub: 

Pubertal

TSS: 

Transcription start site

ZNF: 

Zinc finger

Declarations

Acknowledgements

Not applicable.

Funding

This work was supported by grants from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (to DSB); Fundação de Amparo à Pesquisa do Estado de São Paulo 2015/06281-7 (to MM), and 2013/03236-5 (to ACL); Conselho Nacional de Desenvolvimento Científico e Tecnológico 302849/2015-7 (to ACL); and National Institute of Health 1R01HD084542 and 8P51OD011092 for the operation of the Oregon National Primate Research Center (to SRO and AL).

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

DSB collected and analyzed the human data and wrote the paper. ACL and ACVK designed the project, discussed the data, and wrote the manuscript. MM performed the bioinformatic analysis and wrote the manuscript. AL and SRO designed the monkey’s study, interpreted the results, and wrote the manuscript. CFA ran the qPCRs of the monkey’s hypothalamus. APMC and IN investigated the methylation status of MKRN3 and DLK1 loci. VNB, BBM, and ACL clinically evaluated the patients with CPP. DBM collected the data and studied the MKRN3 gene. MCS collected the data and studied the DLK1 gene. HMCP, EAMRR, and MFB clinically evaluated the control group. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Clinical Hospital of the University of Sao Paulo Medical School (no. 45533215.9.0000.0068), and a written informed consent was obtained from control individuals, patients, and their parents.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Endocrinology & Metabolism, Development Endocrinology Unit, Laboratory of Hormones and Molecular Genetics/LIM42, Clinical Hospital, Sao Paulo Medical School, University of Sao Paulo, Sao Paulo, SP, Brazil
(2)
Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil
(3)
Division of Genetics, Oregon National Primate Research Center/OHSU, Beaverton, OR, USA
(4)
Sorbonne Université, INSERM, UMR_S 938 Centre de Recherche Saint Antoine, APHP, Hôpital Armand Trousseau, Explorations Fonctionnelles Endocriniennes, Paris, France
(5)
Division of Endocrinology, Triangulo Mineiro Federal University, Uberaba, MG, Brazil
(6)
Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, SP, Brazil
(7)
Division of Neuroscience, Oregon National Primate Research Center/OHSU, Beaverton, OR, USA
(8)
Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, Departamento de Clínica Médica, Disciplina de Endocrinologia e Metabologia, Av. Dr. Enéas de Carvalho Aguiar, 255, 7° andar, sala 7037, São Paulo, CEP: 05403-900, Brazil

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