Forensic Epigenetics and Age Determination

Bruce McCord

Florida International University

Miami, FL

mccordb@fiu.edu

The Dutch Winter of

1944

75 years ago Nazi administrators cut food to North Holland in retaliation for a railroad strike, 20,000 people died before relief from allied forces

Pregnant women who recovered from the famine gave birth to children who were smaller than average, suffered from diabetes and had a 10% increase in mortality. The children of their children also suffered from these effects.

The fact is that the famine silenced certain genes (eg PM3 linked to body mass index and metabolism) due to epigenetic mechanisms.

This event provided a clear indication of the role environment plays in epigenetic mechanisms.

Epigenetics

It is obvious to anyone that the human body has many different kinds of cells; skin, hair, teeth, blood, etc

Yet our DNA is all the same, How then does our body differentiate cells? Why do twins have different fingerprints?

DNA methylation patterns in young and

older twins.

Why do identical twins begin to appear different

with age?

Epigenetics

The answer is that there are heritable differences in our DNA that are not related to base pairing.

Instead these differences are controlled by patterns of methylation in cytosine and in post translational modifications of histones.

Epigenetics is the study of heritable changes in gene expression unrelated to DNA base pairing.

Epigenetic Methylation

Methyl residues are covalently bound to the 5’ carbon position of

cytosine pyrimidine ring via DNA methyltransferases (Dnmt) forming

5-methylcytosines

Observed at CpG dinucleotides (70% of CpGs are methylated in

vertebrates but distinct patterns are seen

How to exploit

this forensically?

Find locations near genes that target expression of cellular proteins or examine whole genome array studies

Look in the genome for specific methylated CpG sites (methylation based differences occur in what are called CpG islands.)

Measure differences in methylation that are dependent on phenotype or cell type.

Objective

Locate sites where tissue or phenotypic specific gene expression occurs. Design primers to encompass CpG islands. Extract DNA

Use Pyrosequencing and/or Real time PCR with (HRM) to detect methylation differences

Use statistical and bioinformatic tools to interpret results – determine tissue type, smoking status, age, etc.

Workflow for DNA methylation analysis using pyrosequencing

DNA extraction

& quantification

Bisulfite conversion

PCR

Pyrosequencing

Data analysis

Exploring markers

& primer design

A portion of the sample is removed and treated for bisulfite conversion

unmethylated C’s become U’s

Standard PCR Amplification takes place, unmethylated C’s become T’s

PyroMark Q24 Advanced /Q48 AutoPrep system (QIAGEN)

Identify potential CpG loci based on large scale epigenetic arrays

This can be performed using standard methods using robotic extraction methods

The %methylation of each CpG site is scored and compared

Bisulfite modified PCR

Pyrosequencing:

10

Q48 Pyrogam

file:///Users/Hussain/Desktop/HB-1971-003_1103017_UM_IAS_Pyro_Q48_Autoprep_0616_W

W%20.pdf

Development of a multiplex kit for tissue typing using pyrosequencing

saliva marker

blood marker

semen marker

vaginal secretion marker

PCR with 4 different primer sets

Pyrosequencingwith 4 different

sequencing primers

Multiplex PCR

Our Current Multiplex for Body Fluid IDsimultaneous amplification of loci to preserve DNA extracts

Sohee Cho

Quentin

Gauthier

Gauthier, Cho, Carmel, McCord, Electrophoresis, submitted

Epigenetic phenotyping

(its not just body fluid typing

Age

Environment

Behavior

Diet

Smoker/ non-smoker

Body Mass index

Hair color

Drug abuse

Because certain epigenetic effects are a response

to environment there may be advantages over

genetic phenotyping13

But wait its more complex…..

Will environmental changes alter results?

Mutually exclusive statements?

No, certain environmental affects trigger gene expression – but not changes genes (Lysenkoism)

Suffice to say the mechanisms are complex. Methylation patterns which control important genes are not necessarily environmentally sensitive. Also some CpG sites may be used only during development.

Epigenetics can also be used to detect suspect

lifestyle. Here we show a marker for smoking status

Hussain Alghanim

47%

81%

92%

Multinomial logistic regression (MLR)

analysis using LOO approach for the 4-

CpG assay

16

Type of Model

Accuracy of prediction in blood

Accuracy of prediction in saliva

Smoking group

Current smoker

Former smoker

Never smoker

Total Current smoker

Former smoker

Never smoker

Total

Combined MLR (4 CpGs)

90.0%

66.7%

84.9%

82.7%

86.9%

54.5%

77.8%

71.4%

Estimation of Human Age

17

https://joshmitteldorf.scienceblog.com/2019/02/25/progress-in-methylation-based-aging-clocks/

Determination of Age:

Epigenetic drift?

Certain CpG loci gradually change

methylation status with age

"High CpG density promoters, and in

particular those mapping to developmental

genes, seem to increase in methylation

with age

CpGs located outside these regions tend

to lose methylation with age

Environmental factors may also play a role

The importance of age

determination in forensics

DNA based facial reconstructionmust be artificially aged

http://www.nytimes.com/2015/02/24/science/building-face-and-a-case-on-dna.html

Melanie McCord

Epigenetic determination of suspect age

20

Goals:1- Scan whole epigenome chip studies and identify CpG sites that show linear correlation with chronological age (increase/decrease with aging)

2- Design primers and investigate surrounding CpGs

3 Develop simple models to rapidly determine correlations with age.

Two genetic loci, GRIA2, and NPTX2, which had been among 88 CpGs previously identified in a whole methylome study of 34 male twins aging were examinedBocklandt, PLoS One 2011, 6,e14821.

We analyzed 44 saliva samples and 23 blood samples from volunteers with ages ranging from 5 to 72 years

Initial work with blood and saliva

22

Blood and saliva

samplesDNA extraction

Quantitation

Bisulfite Conversion

AmplificationPyrosequenci

ng

Workflow:

CpG sites with age correlation for GRIA 2 and NPTX2

CpG 1 from GRIA2 and CpG 12 from NPTX2

provided the best correlation with age

GRIA NPTX2

Difference between predicted and observed:6.9

years

9.2 years

Searching for CpG sites

Location: three genes (SCGN, KLF14, DLX5)

No. of sites tested: 27 CpG sites

25

Locus Number of CpG Sites Tested

SCGN 10

KLF14 7

DLX5 10

Results:

Screening Step:

Detection of relevant CPGs at a Locus

Age Prediction Model for Saliva

29

Saliva samples (n= 91, ages 5 and 73 years)

Divided into a 52- person training set and a 39-person validation set.

Analysis via multivariate linear regression to determine mean absolute deviation

Locus CpG sites involved

Training set

Validation set

Adj. R2

MADYears

Adj. R2 MADYears

KLF14 CpG10.851 5.8 0.810 8.0

CpG2

KLF14+

SCGN

CpG1-KLF14

0.840 6.2 0.741 7.1CpG3-SCGN

Sample: Sal-1H, actual age = 51

30

1- Based in single-locus model: Estimated age (in years) =- 24.884 + (1.703 * CpG1 from KLF14) + (1.963 * CpG2 from KLF14)=

KLF14

56

Prediction Accuracy:

Age category

Single-locus Model Dual-locus Model

MAD% Correct prediction

MAD% Correct prediction

1 (6-22 years)2 (23-40 years)3 (41-54 years)4 (55-67 years)Category 1 & 2Category 3 & 4

Overall

4.45.6

12.29.05.110.88.0

100.069.245.555.678.950.064.1

5.05.88.48.65.78.57.1

83.376.954.955.678.955.066.7

31

32

Chronological age versus predicted age of the entire data set of the 91 saliva samples using the single-locus prediction

model (CpG1 and CpG2 from KLF14 )

Age Prediction Model for Blood:

33

LocusCpG sites involved

Training set Validation set

Adjusted R2

MADAdjuste

d R2

MAD

KLF14+ SCGN

CpG2-KLF14

0.708 6.6 0.813 10.3CpG3-KLF14

CpG1-SCGN

• Blood (n=72) (ages 5 and 73 yrs old)

• 40 person-training set and 32 person-validation set.

Significance

The single locus age-predictor model is quick and easy

Other age prediction models require 7 or more separate locations

Very useful to estimate age, especially for younger subjects (40 years)

Resuts can be tissue specific

Publications1. Madi,T.; Balamurugan,K; Bombardi,R.;Duncan, G.; McCord,B. The determination of tissue specific DNA methylation patterns in forensic biofluids using bisulfite modification and pyrosequencing Electrophoresis, 2012, 33(12) 1736-1745.

2. Balamurugan,K.; Bombardi,R.; Duncan,G.; McCord, B., The identification of spermatozoa by tissue specific differential DNA methylation using bisulfite modification and pyrosequencing, Electrophoresis, 2014, 35, 3079-3086.

3. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Evaluation of DNA methylation markers and their potential to predict human aging, Electrophoresis, 2015, 36, 1775-1780.

4. Joana Antunes1, Kuppareddi Balamurugan2, George Duncan1, Bruce McCord1 Tissue specific DNA methylation patterns in forensic samples detected by pyrosequencing, Jörg Tost and Ulrich Lehmann (eds) Methods in Microbiology, Springer, 2015.

5. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples, Forensic Science International: Genetics, 2016,23:55–63.

6. Joana Antunes, Deborah S.B.S. Silva, K. Balamurugan3; G. Duncan, C. S. Alho2, B. McCord. High Resolution Melt analysis of DNA methylation to discriminate semen in biological stains, Analytical Biochemistry, 2016, 494: 40-45

7. Sang-Eun Jung; Sohee Cho; Joana Antunes; Iva Gomes; Mari L. Uchimoto; Yu Na Oh; Lisa Di Giacomo; Peter M. Schneider; Min Sun Park; Dieudonne van der Meer; Graham Williams; Bruce McCord; Hee-Jung Ahn; Dong Ho Choi; Yang-Han Lee; Soong Deok Lee; Hwan Young Lee. A collaborative exercise on DNA methylation based body fluid typing, Electrophoresis, 2016, 37, 2759-2766.

8. Antunes, J. Deborah S.B.S. Silva K. Balamurugan; G. Duncan, C. S. Alho, B. McCord, Epigenetic discrimination of vaginal epithelia using bisulfite modified PCR and pyrosequencing, Electrophoresis,2016, 37, 2751-2758.

9. Alghanim H; Antunes J;Silva D; Alho C, Balamurugan K; McCord B. Detection and evaluation of DNA methylation markers found at SCGN and KLF14 loci to estimate human age, FSI Genetics, 2017,31 81-88.

10. Alghanim, H. , Wu, W. and McCord, B. (2018), DNA methylation assay based on pyrosequencing for determination of smoking status. Electrophoresis 2018, 2018, 39, 2806–2814.

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Acknowledgements

Award 2012-DN-BX-K0182017-BX-NX-0001

Major support for this work was provided by:

The National Institute of Justice

Points of view in the document are those of the authors and do not necessarily represent the

official view of the U.S. Department of Justice

Hussain Alghanim, Deborah Silva, Tania Madi, Kuppareddi Balamurugan, Joana Antunes, Clarice Alho, Quentin

Gauthier, Sohee Cho, Nicole Fernandez TejeroFlorida International University (USA)

University of Southern Mississippi (USA)Catholic University of Rio Grande do Sol (Brazil)Broward Sheriff’s Office Ft Lauderdale, FL (USA)

San Francisco Police Department (USA)

CNPq - Brazil- Conselho Nacional de Desenvolvimento Cientifico e

TecnologicoInstitute of Forensic Science,

Seoul National University”. Qiagen