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2018-10-10 Epigenetic Age Signatures In Saliva: Age Prediction Using Methylation SNaPshot and Massively Parallel Sequencing Sae Rom Hong 1,2 , Sang-Eun Jung 1 , Eun Hee Lee 1 , Kyoung-Jin Shin 1,2 , Woo Ick Yang 1 , Hwan Young Lee 1 1 Department

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2018-10-10 1

Epigenetic Age Signatures In Saliva:

Age Prediction Using Methylation SNaPshot and Massively Parallel Sequencing

Sae Rom Hong1,2, Sang-Eun Jung1, Eun Hee Lee1, Kyoung-Jin Shin1,2, Woo Ick Yang1, Hwan Young Lee1

1Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, South Korea 2Department of Forensic Medicine and Brain Korea 21 PLUS Project for Medical Science, Yonsei University,

Seoul, South Korea

DNA Methylation

  • Addition of a methyl group to cytosine followed by guanine
  • 5’-CG-3’

DNMT Cytosine 5-methyl Cytosine

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2018-10-10 2

DNA Methylation

Cell differentiation Aging Genetic factor Environmental factor

[ ]

Body Fluid Identification

[ ]

Age Prediction

[ ]

Genetic Traits G

[ ]

Behavior Habits

Age Prediction

  • Age-related molecular changes

‒ Telomere shortening ‒ Mitochonrial DNA deletion ‒ sjTREC ‒ DNA methylation

  • DNA methylation-based age predictors

‒ Various tissues

  • Koch & Wagner. Aging (Albany NY) (2011)
  • Horvath. Genome biol. (2013)

‒ Blood

  • Hannum et al. Mol. Cell. (2013)
  • Zbiec-Piekarska et al. FSI Genet. (2015)

‒ Semen

  • Lee et al. FSI Genet. (2015)

‒ Saliva & Buccal swab

  • Bocklandt et al. PLoS One. (2011)
  • Eipel et al. Aging (Albany NY) (2016)
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2018-10-10 3

Method

  • HumanMethylation450 BeadChip Array

‒ 54 males (18-73 years)

  • Targeted Bisulfite Sequencing

‒ Multiplex methylation SNaPshot (226 samples; Both sets) ‒ Massively parallel sequencing (95 samples; Training set) ‒ Multivariate linear regression analysis using SPSS

  • Saliva samples

‒ 280 samples (18-73 years) info Training Set Testing Set Total Male 47 70 117 Female 48 61 109 Total 95 131 226

HumanMethylation450 BeadChip Array

  • Details

‒ 54 males (18-73 years) ‒ GSE92767 ‒ 445,791 CpGs

  • Selection of marker candidates

Criteria

  • No. CpGs

FDR_p < 0.05 74,807 R2 value > 0.65 80 |β-scoreMAX – β-scoremin | ≥ 0.1 62 Advancing age

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Age-associated CpG candidates

Stepwise linear regression analysis Additional candidates

+

R² = 0.9286

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Methylation at cg18384097 Buccal-Cell-Signature (ϐ) (predicted epithelial cell compositions) N = 54 Spearman’s rho = 0.955

Cell Type-specific Marker

  • cg18384097 (PTPN7)

‒ Souren et al. Genome Biol. (2013) ‒ High in buccal epithelial cell ‒ Low in blood cell ‒ PTPN7 gene

  • Protein tyrosine phosphatase (PTP)
  • Preferentially expressed in hematopoietic cells
  • Buccal-Cell-Signature (ϐ)

‒ Eipel et al. Aging (Albany NY). (2016) ‒ cg07380416 (CD6) ‒ cg20837735 (SERPINB5) ‒ Percentage of buccal epithelial cells

ϐ = 99.8 × + 1.92 2 + −98.12× + 88.54 2

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2018-10-10 5

Massively Parallel Sequencing (N=95)

DNA Methylation Analysis

Multiplex PCR

Read Sequence

Indexing PCR

Index Sequence

Detail Workflow

10ng

Bisulfite conversed DNA Multiplex Methylation SNaPshot (N=226=95+131)

Multiplex PCR

DNA Methylation Analysis

Multiplex SBE

A G

Hong et al. FSI Genet. (2017) Lee et al. FSI Genet. (2016)

Methylation SNaPshot (N=95)

R² = 0.5412 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg00481951 (SST)

R² = 0.2541 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg19671120 (CNGA3)

R² = 0.6313 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg14361627 (KLF14)

R² = 0.4193 0.2 0.4 0.6 0.8 1 20 40 60 80 Methylation Chronological Age (years)

cg08928145 (TSSK6)

R² = 0.2139 0.1 0.2 0.3 0.4 0.5 0.6 20 40 60 80 Methylation Chronological Age (years)

cg12757011 (TBR1)

R² = 0.5928 0.1 0.2 0.3 0.4 0.5 0.6 20 40 60 80 Methylation Chronological Age (years)

cg07547549 (SLC12A5)

Methylation SNaPshot (N=226)

R² = 0.4791 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg00481951 (SST)

R² = 0.2882 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg19671120 (CNGA3)

R² = 0.6347 0.1 0.2 0.3 0.4 20 40 60 80 Methylation Chronological Age (years)

cg14361627 (KLF14)

R² = 0.4341 0.2 0.4 0.6 0.8 1 20 40 60 80 Methylation Chronological Age (years)

cg08928145 (TSSK6)

R² = 0.167 0.1 0.2 0.3 0.4 0.5 0.6 20 40 60 80 Methylation Chronological Age (years)

cg12757011 (TBR1)

R² = 0.5486 0.1 0.2 0.3 0.4 0.5 0.6 20 40 60 80 Methylation Chronological Age (years)

cg07547549 (SLC12A5)

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2018-10-10 6

Model – Multiplex Methylation SNaPshot

Target ID Coefficient (intercept)

  • 24.521

cg18384097

  • 31.111

cg00481951

6.718

cg19671120

23.760

cg14361627

81.053

cg08928145

24.325

cg12757011

53.634

cg07547549

89.415

20 40 60 80 20 40 60 80

Predicted Age (years) Chronological Age (years)

Training Set (N=95)

20 40 60 80 20 40 60 80

Predicted Age (years) Chronological Age (years)

Testing Set (N=131) MAD = 3.03 RMSE = 4.03 MAD = 3.43 RMSE = 4.36

MAD: Mean Absolute Deviation RMSE: Root Mean Square Error

MPS (N=95) – Read Count

500,000 1,000,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 500,000 1,000,000 25 26 27 28 29 30 31 32 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 500,000 1,000,000 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 500,000 1,000,000 74 75 76 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 100 AVG 30000 60000 cg18384097 cg00481951 cg19671120 cg14361627 cg08928145 cg12757011 cg07547549

Read count per sample AVG depth per marker

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2018-10-10 7

R² = 0.5808 0.1 0.2 0.3 0.4 0.5 20 40 60 80 Methylation Chronological Age (years)

cg07547549 (SLC12A5)

R² = 0.661 0.1 0.2 20 40 60 80 Methylation Chronological Age (years)

cg00481951 (SST)

MPS (N=95) – Methylation value

R² = 0.3209 0.1 0.2 20 40 60 80 Methylation Chronological Age (years)

cg19671120 (CNGA3)

R² = 0.5738 0.1 0.2 20 40 60 80 Methylation Chronological Age (years)

cg14361627 (KLF14)

R² = 0.4047 0.2 0.4 0.6 0.8 1 20 40 60 80 Methylation Chronological Age (years)

cg08928145 (TSSK6)

R² = 0.2218 0.1 0.2 0.3 0.4 0.5 20 40 60 80 Methylation Chronological Age (years)

cg12757011 (TBR1)

SNaPshot vs MPS (N=95)

G (Methylated) A (Unmethylated)

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2018-10-10 8

  • 20

20 40 60 80 20 40 60 80

Predicted Age (years) Chronological Age (years)

SNaPshot vs MPS (N=95)

MPS SNaPshot

SNaPshot vs MPS (N=95)

Target ID Coefficient (intercept)

  • 24.521

cg18384097

  • 31.111

cg00481951

6.718

cg19671120

23.760

cg14361627

81.053

cg08928145

24.325

cg12757011

53.634

cg07547549

89.415

Model - SNaPshot

20 40 60 80 20 40 60 80

Predicted Age (years) Chronological Age (years)

MPS model (N=95)

Model – MPS

Target ID Coefficient (intercept)

  • 7.282

cg18384097

  • 18.170

cg00481951

131.995

cg19671120

71.822

cg14361627

138.619

cg08928145

20.377

cg12757011

  • 1.307

cg07547549

78.467 MAD = 3.67 RMSE = 4.80

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2018-10-10 9

Conclusion

  • A cell type-specific marker (cg18384097) and 6 age-associate

markers (cg00481951, cg19671120, cg14361627, cg08928145, cg12757011, and cg07547549) enabled age prediction in saliva with high accuracy.

  • Markers can

be applied to both Multiplex methylation SNaPshot and MPS.

  • The model should be altered as the platform differs.

Acknowledgement

Yonsei DNA Profiling Group This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government (NRF- 2014M3A9E1069992).