Methylation Marker – Future Of Chronological Age Estimation In Forensics

Abstract

DNA methylation is an epigenetic mechanism which plays a vital role in the control and regulation of gene expression. In the past few years DNA Methylation was widely investigated in Medical field to understand complex diseases, but of late there has been a surge in studies exploring its potential use in Forensic Science. Telomere length are significant marker for age estimation however, biological aging is a complex process and telomere length is unable to inform of all its aspects, making it an inaccurate biomarker in many instances.

Recently, several new biomarkers of age of increased accuracy and content have been developed. One of these new biomarkers is made by combining the levels of multiple biochemical routine blood tests (Putin et al., 2016), others include information on the expression of hundreds of genes (Peters et al., 2015). The most accurate of all them are the epigenetic biomarkers (Weidner et al., 2014; Peters et al., 2015; Putin et al., 2016), which have become possible after the identification of age associated changes in DNA methylation at specific CpG sites. This new forensic approach analyzes the chemical tags attached to DNA, rather than genetic sequences themselves.

These molecules, which can switch genes on and off, get added onto DNA throughout our life span in a process called DNA methylation and because the patterns of DNA methylation change as we age, they could provide a good indication of how old a suspect is.DNA methylation biomarkers along with the present day age estimation techniques from skull (including teeth) and pevis could be used simultaneously for the accurate measurements of the mean eastimations of age from these three methods. This might be contained within a software where a quantitative mean estimation of age prediction can be done. This software development relies on extensive future research, but can be time, labour, and cost-effective since 3 of them will be combined in a single step analysis. Key words: Epigenetics; DNA methylation; age estimation.

Introduction

Some of the methods routinely used are based on dental morphology and bone elongation/epiphyseal ossification, but chemical methods involving aspartic acid racemization, lead accumulation, collagen crosslinks, chemical composition of teeth, and glycosylation of proteins have also been promising as potential novel age estimation methods. On the molecular biology front, telomere shortening, mitochondrial mutations, and single joint T-cell receptor excision circle rearrangements were initially greeted with enthusiasm, but the low accuracy of these assays has quickly diminished hope for these methods to become routinely implemented in forensic investigations.

DNA methylation appears to have less biological variation and better resolution than skeletal and tooth maturation. Moreover, unlike skeletal and tooth maturation, DNA methylation has no end stage. Another advantage of DNA methylation is that international research activity in many fields is adding rapidly increasing amounts of knowledge and freely available data. Only a small quantity of blood or saliva is required for the analysis, and this also makes the method more ethically acceptable in both research and practical use than today’s radiological methods.

Review of Literature

1. Gregory Hannum et al. (2012) They build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101 to estimate the rate at which the methylome of an individual ages, which actually correspond with the pattern of transcription. These markers are predictive of age across a range of different tissue types and organisms. They performed genome-wide methylomic profiling of a large cohort of individuals and based on their finding, an aging rate predictive model was constructed. With the initiation of such scientific researches in the field of genetics, a base for the future challenges relating to DNA based age estimation was set.
2. Ja Hyun , Shin, Choi , Yang and Lee (2013). They stated that at molecular levels, there are some alterations in tissues and organs which are a result of aging. These alterations are the base for the forensic scientists to develop out methodologies to estimate the age of a living person or a dead body. Initially the focus was at the genes which were thought to be the factor in aging process but it was eventually the epigenetic mechanisms (gene expression mechanism) especially for genes that function in metabolic and DNA repair pathways. MSP: Methylation specific PCR; COBRA: Combined bisulfite restriction analysis of DNA; MS-SnuPE: Methylation-sensitive single nucleotide primer extension; MSRE-PCR: Methylation-sensitive restriction endonuclease PCR.
3. Horvath S (2013). He developed an age predictor which was capable of estimating the age in most of the tissues and the cell types using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. Some of the highlights of the findings made on DNA- methylation was:a. It was close to zero for embryonic and induced pluripotent stem cellsb. It was correlated with cell passage numberc. It gave rise to a highly heritable measure of age accelerationd. It was applicable to chimpanzee tissues. According to him this novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
4. Bekaert, Kamalandua, Zapico, Voorde, and Decorte (2015). They emphasized on Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. They selected 4 age-associated genes (ASPA, PDE4C, ELOVL2, and EDARADD) and determined CpG methylation levels from 206 blood samples of both deceased and living individuals (age range: 0–91 years). Correlation between CpG methylation of all 5 genes (ASPA, ITGA2B, PDE4C, ELOVL2, and EDARADD) and age was first assessed for 36 of the 206 blood samples in an exploratory phase. Methylation of CpG sites in ASPA and EDARADD was negatively correlated with age, whereas positive correlations were found between methylation and age for PDE4C and ELOVL2.
5. Vidal, Lopez and Gonzalez (2016). This published article states epigenetic biomarkers to be the most accurate biological age markers that were based on DNA methylation referred as DNA methylation age measures (DmAM). A new DmAM study was conducted in whole blood (WB) of 8 CpG sites selected as the most informative on a training set of 390 healthy subjects. The 8 CpG DmAM showed better accuracy than other DmAM based in few CpG in an independent validation set of 335 subjects. Since, it is possible to study the new 8 CpG DmAM in a single multiplex reaction with methylation-sensitive single-nucleotide primer extension (MS-SNuPE), a methodology based on commercially available reagents and run in capillary electrophoresis sequencers, high cost of DNA methylation microarrays was avoided.
6. Vidaki, Ballard, Alliferi, Miller, Barron, Court (2017). The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. Various genome-wide methylation analyses have revealed a substantial decrease in global DNA methylation levels with advancing age. Changes in DNA methylation patterns due to aging are quickly observed during the first months of an individual’s life and throughout childhood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous study. 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2 = 0.92, mean absolute error (MAE) = 4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2 = 0.96) with a MAE = 3.3 years.
7. Aradas, Phillips and Lareu (2017). This review assessed the most widely adopted genes harboring methylation sites, detection technologies, statistical age-predictive analyses, and potential causes of variation in age estimates. Forensic age determination was originally based on the examination of bones and teeth but in the recent years more and more researches are being carried, based upon the variability shown by the epigenetic biomarkers, including DNA methylation which is the best understood epigenetic signature in the genome. It is a chemical modification that, in mammalian genomes, predominantly occurs at the 5’ carbon atom [C5] at cytosine residues that are followed by guanine (termed CpG dinucleotides), leading to 5-methylcytosine.

Several current models for age prediction in forensic are discussed in this paper telling that first application of DNA methylation for age estimation using a discrete number of CpG sites was that of Weidner et al. in 2014. The study comprised of 3-CpG model comprising one CpG site located in three independent genomic regions. These 3 identified sites: cg02228185 (ASPA), cg25809905 (ITGA2B), and a CpG site close to cg17861230 (PDE4C), were used to construct an age-prediction model from the 132.

Shyamalika Gopalan et al. (2017). Previousl identifications of CpG sites which have strong correlation with the chronological age have primarily focused on the population living in the developed world however, it was not not known if age-related patterns of DNA methylation at these loci are similar across a broad range of human genetic and ecological diversity. So Gopalan and her fellows investigated genome-wide methylation patterns using saliva- and whole blood-derived DNA. They identified a strong hypo-methylation trend with age in the gene DDO, particularly in saliva tissue found in body. The enzyme which is coded by DDO deaminates D- aspartic acid and non-enzymatic accumulation of D-aspartic acid in living tissues is age dependent and is so pronounced in tissues with low turnover that it has been proposed as a biomarker for aging (Helfman and Bada 1975).

De- Paoli Iseppi et al. (2017). Since animal age is of huge importance to the environmentalists to estimate and infer the changes that take place in an ecosystem. This paper studies the age estimation pattern of the genome of the animals including humans.In this mini- review, they summarise current knowledge of observed age-related changes of CpG DNAm in mammals, reptiles, birds and fish.

Matt Warren (2018). According to this research article, the novel methodologies of DNA- based suspect investigations rely on studies that analyzes the chemical tags attached to DNA, rather than genetic sequences themselves and These molecules(tags) , which can switch genes on and off, get added onto DNA throughout our life span in a process called DNA methylation and change in the pattern of methylation throughout an individuals life is the key to the present methodology. Since these methods are capable to reveal a lot more about a suspect’s lifestyle (methylation has also been associated with risk of mortality and all kinds of other pathologies), legal and ethical questions might get raised so it is to be ensured that new privacy safeguards must be established and regulations should be more restrictive in terms of what you can do with DNA samples.

Vidaki , kayser (2018). Differerential rate of DNA methylation among tisuues and individuals are of extreme importance to forensic scientists as it has a number of forensic application including:

• determining the tissue type of a human biological trace,
• estimating the age of an unknown trace donor,
• differentiating between monozygotic twins.

The paper also summarizes the most recent literature relating to the above three topics of forensic epigenetic investigations wherein, the limitations and practical considerations of the method is being given a serious thought. Gene expression regulation via DNA methylation ‘switches’, where a methylated gene promoter (black lollipops) usually leads to gene inactivation by ‘blocking’ the transcription mechanism, DNA methylation variation within different tissues (blood and saliva) of the same individual, where DNA methylation differences occur either in a tissue-specific or tissue-shared manner, in various locations associated with a gene, DNA methylation variation between individuals of the same monozygotic twin pair, which can be stochastic, disease-related (red arrows), or due to lifestyle choice differences, and future concept of epigenetic fingerprinting, where differential DNA methylation profiling can lead to the simultaneous prediction of a stain’s tissue source and an individual’s age and lifestyle choices useful for crime scene investigation and investigative intelligence.