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Forensic Epigenetics and Age Determination
Bruce McCord
Florida International University
Miami, FL
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.
.
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