Exploring the utility of genetic markers for predicting biological age

Legal Medicine 14 (2012) 279–285

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Legal Medicine journal homepage: www.elsevier.com/locate/legalmed

Exploring the utility of genetic markers for predicting biological age Maria Saeed a, Rebecca M. Berlin a, Tracey Dawson Cruz a,b,⇑ a b

Virginia Commonwealth University, Department of Forensic Science, 1000 W. Cary St., P.O. Box 842012, Richmond, VA 23284, USA Virginia Commonwealth University, Department of Biology, 1000 W. Cary St., P.O. Box 842012, Richmond, VA 23284, USA

a r t i c l e

i n f o

Article history: Received 21 March 2012 Accepted 31 May 2012 Available online 6 July 2012 Keywords: Forensic science DNA Telomere length Chromosomes Age estimation

a b s t r a c t DNA evidence can be analyzed for genetic markers to determine phenotypes such as hair and eye color, ancestry, and even age estimation. Currently, telomere length is the only genetic biomarker that has been correlated to cell replication and replicative cell senescence – both strong indicators of tissue aging in humans. Unfortunately, while many studies have found a strong correlation between telomere length and age, many data sets show extreme variability, technical assay malfunction, inadequate evaluation of other variables that can impact telomere, altogether conflicting results, or insignificant correlations due to low sample size. Other, non-telomere based methods are problematic, as they often have only the ability to identify newborns or are only viable for specific tissue or cell types, and for most, the effects of outside variables have not been fully evaluated. Thus, telomeres remain the most promising biomarker for age estimation; mechanisms for telomere repeat attrition over time have been well documented. Unfortunately, assays currently used determine mean telomere length of a sample, are not precise or reproducible. New techniques should be robust enough to determine age across a broad spectrum of age ranges, and the effect of other variables (gender, race, disease, etc.), must be explored. Ó 2012 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2.

3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age and telomere length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Telomerase and telomere attrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Tissue-specific telomere attrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Chromosome-specific telomere attrition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Variables impacting telomere shortening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Additional methods for age estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Non-telomere based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Recent advances in forensic DNA analysis have allowed scientists to obtain unique genetic profiles of individuals from DNA extracted from biological evidence found at crime scenes. This is beneficial because the unknown profile can be compared to reference samples provided by the victims and/or suspects. Also, they ⇑ Corresponding author at: Virginia Commonwealth University, Department of Biology, 1000 W. Cary St., P.O. Box 842012, Richmond, VA 23284, USA. Tel.: +1 804 828 0642; fax: +1 804 828 0503. E-mail address: [email protected] (T.D. Cruz). 1344-6223/$ - see front matter Ó 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.legalmed.2012.05.003

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can be compared against all profiles in a state or national databank to determine the identity of the donor. However, when the donor cannot be identified, the scientist cannot help the investigator regarding what physical characteristics to look for in a suspect, other than gender [1]. One important phenotypic characteristic that could aid the investigator is age of the possible suspect. Medical examiners and anthropologists have been able to determine the age of an individual by examining dental records and measuring skeletal remains [2]. This is useful only in cases where the body is present and the identity of the victim is unknown. In most cases, however, the perpetrator has fled after committing the crime, leaving behind

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biological evidence such as blood, semen, saliva, and epithelial cells, which is analyzed to obtain a DNA profile of the donor. Several studies over the years have focused on telomere length as a molecular tool for estimating age. Telomeres are found at the end of chromosomes in eukaryotes and their purpose is to protect the DNA from damage [3,4]. At birth, they are composed of about 15–20 thousand base pair repeats of the TTAGGG sequence. However, during replication of the chromosomes, DNA polymerase is unable to replicate the ends of the lagging strand, which causes the telomere sequence to shorten over time, and the cell reaches senescence, or cell death [3–9]. To accommodate this, a reverse transcriptase enzyme (telomerase) elongates telomeres by adding repeat units to the end of the strands and delaying senescence [10,11]. It has been reported that individuals with shorter telomere length have lower survival [12]. Similarly, Bakaysa et al. reported in their monozygotic and dizygotic twin study that all twins with shorter telomere length have three times more risk of early death than their co-twins with longer telomere length [13]. Given this data, many recent studies have focused on the viability of telomere length as a molecular marker for estimating the age of living persons [4,5,14]. However, conflicting reports have also been published regarding their reliability and reproducibility [1,6,9,15]. Furthermore, even if a correlation does exist, telomerase activity in forensically relevant tissues, such as epithelial and sperm cells could potentially impact the viability of telomere length as a biomarker for age [16–18]. Similarly, studies have shown that physiological and pathological conditions, such as stress and disease, can also affect telomere length [11,19–23]. Thus, it is important to determine if and how these issues impact a forensic scientist’s ability to estimate age using telomere length.

2. Age and telomere length In a 2002 study by Tsuji et al., blood samples were collected from individuals (n = 60) between the ages 0 and 85. A Southern blot analysis [24] was conducted to determine the terminal restriction fragment (TRF) length of telomeres in a cell sample. The probe used with this method was labeled with degoxigenin (DIG) and was specific for the TTAGGG telomeric repeat sequence [24]. In this case, the researchers found an average decrease in TRF length with an increase in age (R2 = 0.692) and fit the following formula to their results: age = 0.0095y + (148.9 ± 7.037) where y represents the average TRF length [4]. Similarly, the Southern blot method [24] was used by Takasaki et al. to determine the TRF length in DNA obtained from the dental pulp of 100 individuals ranging from 16 to 70 years old. Based on their data, the correlation from their results was the following: age = 0.0119y + 168.0 ± 7.52 (R2 = 0.562) [14]. Ren et al. investigated age related changes in telomere length (using the same method) by analyzing DNA from human peripheral blood (PBL) obtained from 105 individuals between the ages of 0 and 81 years. Their results fit the following formula: age = 16.539y + 236.287 ± 9.832 (R2 = 0.834) [5]. However, these results show a clear correlation between telomere length and biological age because telomere sizes differ based on chromosome [4,5,14] and thus, this method results in wide variation within a single sample. It is important to note that, in these studies, the mean of all TRF lengths was measured with the Southern blot method. While all the studies that used Allshire’s Southern blot method [24] found a strong correlation between telomere length and age, they all discussed the observed variability among the data sets. Tsuji et al. informally attributed the variability in the data to environmental and genetic factors (such as disease), whereas Ren et al. concluded that TRF lengths did not differ between the two ethnic

populations that were tested (Tibetan and Han), but instead noted that the differences in the data were likely due to gender. In fact, they determined in a separate study that males, on average, have shorter TRF lengths than females [5]. Variations aside, both studies ultimately claimed that TRF length is a useful tool for determining the age of a donor from biological samples, especially when morphological information is not otherwise available [4,5]. Although the Southern blot method is useful, it is also tedious and difficult to quantify. Thus, in most recent studies, researchers have switched to a faster and more efficient technique to determine telomere length [1,6,15,25]. In 2002, Cawthon developed a new method using real-time PCR (qPCR) which requires a smaller amount of DNA and is much less time consuming than the Southern blot method. This method determines the difference between the DNA sample and a reference DNA sample by measuring the ratio of telomere repeat copy number (T) to a known single copy gene copy number (S), which is directly proportional to the average telomere length measured by the Southern blot technique [25]. In this qPCR method, a fluorescence-based primer assay was developed for the qPCR method that eliminated the potential primer–dimer products seen between the TTAGGG and CCCTAA repeats [25]. Since the development of this technique, most subsequent age estimation studies have used Cawthon’s method (or a modified method) to measure average telomere length [1,6,15]. In a study published by Karlsson et al., a total of 96 samples were obtained from individuals between the age of 0 and 86, and Cawthon’s qPCR method was used to measure telomere length. When the T/S ratios were plotted against age, the correlation (R2 = 0.3) was much lower than the previous three age estimation studies (mentioned above). The standard error of age prediction was also estimated to be much higher (±22 years) than found previously. Additionally, they discovered differences in telomere lengths when comparing DNA from blood swabs against DNA from buccal swabs, the latter of which contained longer telomeres (likely due to telomerase activity). Therefore, based on their findings, these authors concluded that age estimation based on analysis of telomere length using qPCR was not appropriate for forensic applications [6]. In another study, telomere length from buccal cells (collected from 167 donors between the ages of 1 and 96 years) was measured by Hewakapuge et al. The researchers used Cawthon’s qPCR method with modifications that included a decrease of input DNA from 35 to 5 ng and an increase in the number of cycles from 20 to 50 [15,25]. The relationship between age and telomere length resulted in an R2 value of 0.037, which the researchers concluded was significantly low (P < 0.05) and thus, not useful for forensic prediction of age. The accuracies of age estimation remained low when the groups were divided by race and gender. Furthermore, there were differences in telomere lengths within same age groups including variation seen among 10 26-year olds and nine 54-year olds. However, a non-linear decrease in telomere length was seen with age when the data was divided into age groups of 10 years [15]. A summary of this study, and those mentioned previously, is shown in Table 1. In a similar study, Ballantyne utilized three methods of determining telomere length to explore correlation with age – Cawthon’s traditional qPCR method (including a fluorescence based SYBRÒ Green assay), a modified qPCR method (using a TaqMan assay), and single telomere length analysis (STELA) of the XpYp telomere. The results that were obtained when the traditional Cawthon method was used indicated that DNA samples provided by the 91-year old individual (n = 1) displayed lower fluorescence (dCt = 3.360) than that of the 45-year old individual (dCt = 2.050). The dCt values for all other individuals (n = 10), ranging in age from newborns and juveniles to adults and the elderly, were between those of the 45 and 91-year olds which was not as expected [1].

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M. Saeed et al. / Legal Medicine 14 (2012) 279–285 Table 1 Correlation studies between genetic markers and biological age. Study Tsuji et al. (2002) [4] Takasaki et al. (2003) [14] Karlsson et al. (2007) [6] Hewakapuge et al. (2008) [15] Ren et al. (2009) [5] Zubakov et al. (2010) [76]

Sample size 60 100 96 167 105 195

Correlation with age 2

R = 0.692 R2 = 0.562 R2 = 0.3 R2 = 0.037 R2 = 0.834 R2 = 0.835

For the modified qPCR amplification technique, two specific primer and probe sets were created, consisting of long and short telomere repeat sequences, to anneal to longer and shorter telomeres, respectively. Therefore, it was expected that a higher fluorescent signal would result for younger individuals having longer telomeres [1]. With the modified qPCR TaqMan method, DNA analyzed from the newborn (assumed to have longer telomere length), the 89-year old, and 91-year old (assumed to have shorter telomere length), all displayed low fluorescence signals and generated an average cycle threshold (Ct) value of 9.73. In this study, individuals between the ages of 4 and 63 years old (n = 4) had a lower average Ct value (8.63), potentially indicating the presence of longer telomeres. However, when the non-template control was tested, a fluorescent signal was detected due to an interaction between the primer and probe. Therefore, the qPCR results obtained from this study were deemed unreliable [1]. STELA, a common method for age estimation, is used to measure the telomere length of specific chromosomes, especially those with shorter telomeres. This method was originally developed to measure XpYp has also been successfully used to size the following chromosomes: 2p, 7q, 11q, 12q, 16q, 16p, and17p [1,26,27]. Age estimation using STELA (for the XpYp telomere) was also unsuccessful because there was inconsistent amplification among the samples (n = 16) and the biological age of the sample donors did not seem to correlate with the resulting amplified telomere lengths [1]. Like the Southern blot method [4,5,14,24], the commonly used qPCR techniques [1,3,6,15,25] and the flow FISH technique [28] for measuring telomere length are also a measure of the average telomere length of all chromosomes in a sample isolate. Therefore, it is likely that a very strong correlation was not seen in some of the above studies that utilized these techniques due to the inherent variability of telomere lengths found on different individual chromosomes within a single cell [1,6,15,28]. A study exploring the correlation between single chromosome telomere length, attrition, and age may better determine the exact strength of the correlation between age and telomere length. 2.1. Telomerase and telomere attrition Telomerase is a ribonucleoprotein as well as a reverse transcriptase (RT) enzyme that elongates telomeres by adding the TTAGGG repeat units to the 30 end of the chromosomal strands to compensate for the loss of telomeric DNA during replication [7]. It is composed of a functional RNA component (hTER) which acts as a template for catalyzing the addition of the telomeric repeats. The other subunit, the telomerase reverse transcriptase (hTERT), contains a catalytic component and has substrate binding sites for DNA and hTER. The separate subunits of telomerase are assembled together by telomerase-associated protein (TPI), allowing telomerase to become active [10,29–33]. When utilizing telomere lengths as biological markers for aging, it is critical to know which cells and chromosomes are susceptible to telomerase activity and the affect of this telomerase activity on telomere lengths over time, so that those cells and chromosomes can then be studied to determine whether telomerase activity

Standard error (years)

Target

Method

±7.037 ±7.52 ±22 – ±9.832 ±8.9

Telomeres Telomeres Telomeres Telomeres Telomeres sjTRECs

Southern blot Southern blot qPCR qPCR Southern blot qPCR

affects overall telomere attrition. It is known that the activity of telomerase is not consistent in all cells and tissues [34,35]. Also, telomerase is not detectable in somatic cells but is detectable and active in male germ (sperm) cells, the basal layer of the epidermis, embryonic stem cells, and cancerous cells [16,17]. The fate of different cells is based on whether the hTERT protein is present or absent, and the expression of the hTERT protein in certain cells is due to the presence of positive and negative regulators, including certain hormones and transcription factors [10,36]. The most common way to measure telomerase activity was described by Kim et al. using a telomeric repeat amplification protocol (TRAP). In the TRAP assay, a protein extract is prepared and primers and dNTPs are added. The oligonucleotide of the telomeric repeat (TTAGGG) is replaced by a substrate primer with a different repeat sequence. This allows the reverse primer to specifically amplify the sequence elongated by telomerase, and those products are detected using gel electrophoresis [37,38]. Initially, results of this original TRAP method were strictly qualitative, depending on the presence or absence of telomerase [37]. However, modifications to this method have been made so that quantitative results are obtained using radioactive materials and gel electrophoresis, or a TaqMan based assay for real-time PCR (qPCR) quantitation [39,40]. 2.1.1. Tissue-specific telomere attrition In a study of stem cells, Engelhardt et al. discovered that telomerase is actively up-regulating the telomere sequence numbers. However, even in the presence of telomerase, they still reported a net average telomere loss of 1–2 kb after four weeks of culture [41]. Studies by Vaziri et al. and Lee et al. also displayed results of an overall decrease of telomere length (9 base pairs per year for Vaziri et al.) as age increased, for in vivo telomerase-active hematopoietic stem cells (HSC). The researchers attributed their findings to the weakness in telomerase activity in comparison to telomere shortening in HSCs [34,42]. In a study by Ulaner and Giudice, it was found that telomerase activity in the lungs, liver, spleen, and testis of fetal tissue (obtained from terminated pregnancies) was not displayed past 21 weeks. Telomerase was active in the brain and kidney of fetal tissue only up to the 16th week and only the 12th week for heart tissue [43]. These results are significant because they demonstrate that telomerase activity is tissue-specific and regulated by catalytic ribonucleic protein (hTERT) which is assumed to become active when cells are differentiated [43]. In a different study by Takubo et al. 14 different telomerase-active organ and tissue types of 137 individuals were analyzed for telomere attrition over one year using the Southern blot method [24]. The results varied for each sample, ranging from 9 to 147 bp average decrease in telomere length per year. However, the researchers noted that there was no significant decrease in telomere length for cerebral cortex and myocardium, suggesting that telomerase is actively renewing cells in those tissues. They further concluded that there was no correlation between telomere length and telomerase activity, and the variation of telomere length in different organs and tissues was characteristic to the different individuals [35]. Several studies found that telomerase

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Table 2 Summary of attrition rates by donor age. Study

Tissue typea

Attrition rate (bp/year)

Donor age (year)

Hastie et al. (1990) [82] Vaziri et al. (1993) [83] Vaziri et al. (1994) [42] Slagboom et al. (1994) [84] Mondello et al. (1999) [85] Rufer et al. (1999) [68]

PBL PBL HSC PBL PBL PBL PBL PBL PBL 14 Tissues and organs PBL PBL PBL PBL PBL PBL

33 41 9 31 42.6 1088 52 20 38.3 9–147

20–85 0–107 – 2–95 26–72 0–1.5 15–90 18–98 0–82 0–104

14 51.3 19.8 21.5 76 68

60–97 30–49 40–80 30–80 20–50 51–68

von Zglinicki et al. (2000) [86] Bestilny et al. (2000) [87] Takubo et al. (2002) [35] Cawthon et al. (2003) [12] Unryn et al. (2005) [52]

Guan et al. (2007) [54] a

Abbreviations: PBL, peripheral blood lymphocyte; HSC, hematopoietic stem cell.

activity present in tissues (such as epithelial cells found in the basal layer of the skin, endometrial tissues, and hair follicals) does not have a sufficient impact on the overall decrease of telomere length, though the rate of decrease may be slower than that found in the tissues that are not known for telomerase activity [16,44–50]. Several studies have analyzed peripheral blood lymphocytes (PBL) (known to respond to telomerase activity) to determine if a net loss (attrition) occurs in telomere length as individuals grow in age [51–53]. Frenck et al. collected PBL samples from 75 donors from 12 different families. This included 12 newborns (less than an hour old), 24 parents (ages 20–36 years), 35 grandparents (ages 42–72 years), and 4 great-grandparents (ages 62–82 years). Using the Southern blot method [24] the researchers discovered that the average length of telomeres significantly decreased from 16.4 ± 1.2 kb in newborns to 11.6 ± 1.2 kb in parents. Similarly, the average TL length decreased for grandparents and great-grandparents (9.6 ± 0.8 and 8.0 ± 1.1 kb, respectively). They also concluded that the attrition rate (>1 kb per year) is high for young children (between 5 and 48 months old) until the age of 4 when the rate is reaches a plateau, which is seen until early adulthood, when gradual attrition begins [51]. In addition, they noted that the attrition pattern did not differ between the 12 families that were a part of the study [51]. In a later study Unryn et al., to determine the affects of telomerase activity in peripheral blood mononuclear cells, the researchers used the Southern blot method [24] and discovered that there was an overall decline in telomere length (between 13 and 29 bp per year) for individuals between the ages of 30 and 80 years old, despite telomerase activity. When the data was divided into two groups, based on individuals’ age, their mean attrition rate for 30–49 and 40–80 year olds was 51.3 bp per year and 19.8 bp per year, respectively [52]. The study also compared the results to the results of several previous studies and reached a similar conclusion as Frenck et al. that attrition rates are higher for younger individuals but a gradual decline of telomere length occurs through an individual’s lifespan, especially in adults over the age of 50 [52]. Similarly, in the study by Guan et al. results showed an average attrition rate of 76 base pairs per year for individuals below the age of 50 years. However, the rate decreased to 68 base pairs per year for individuals older than 50 years [54]. Overall, this suggests that there is a net decrease in telomere length over time, regardless of change in telomere attrition rate and telomerase activity levels. Attrition rates from 12 studies are summarized in Table 2. In a most recent study of 959 PBL donors, Nordfjall et al. discovered that there was a mean decrease in average telomere length

over 10 years. However, when results were evaluated for each individual, about 34% of individuals tested displayed stable or increased telomere length over the 10 year span of the study, indicating that for most (66%) individuals, a net gain in telomere length was not seen despite telomerase activity [53]. Also, the researchers noted that the attrition rate was higher for individuals with longer telomere length during year 1 of the 10 year study, and they attributed this to telomerase maintenance of telomeres in vivo for those individuals, suggesting that telomere attrition varies between individuals [53]. When attrition rate was calculated for 13 families (with 10 or more individuals) that were included in the study, a significant net decrease in telomere length (r = 0.691) was observed over the 10 year study [53]. One very important, forensically relevant cell type is the sperm cell, which is most often found in rape and sexual assault cases and is known to express telomerase activity. In a 1992 study by Allsopp et al., the mean TRF length was measured for the sperm of 63 donors (19–68 years old). The results showed an increase in telomere length of 71 bp per year [55]. Similarly, in a recent study, Kimura et al. analyzed telomere length of spermatozoa from younger men (less than 30 years of age) and compared it to the telomere length of sperm from older men (above the age of 50) using the Southern blot method [29,56]. The results of this study indicated that sperm of older men on average have longer telomeres than that of younger men [56]. However, the researchers suggested these results were influenced by heredity of the individuals, telomerase activity in sperm cells, and their resistance to oxidative stress [56]. They also concluded that further research must be conducted in this area using a wider age range of individuals and using a method for analyzing subsets of chromosomes with longer telomere lengths individually versus those with shorter telomere lengths rather than using a method that results in an average measure across all chromosomes [56].

2.1.2. Chromosome-specific telomere attrition It has been shown in several studies that telomerase activity favors chromosomes with shorter telomeres [57,58]. In the study by Oullete et al., human fibroblasts expressing hTERT were examined for up to 400 population doublings. The researchers saw that telomerase activity decreased over a long term period. However, telomeres that were, on average, shorter than the parent senescent cells, maintained their size due to telomerase activity while longer telomeres shortened with time. The researchers concluded that the decrease in the average telomere size was due to the attrition of longer telomeres rather than shorter telomeres [57]. Seinert et al. found similar results in their 92 population doublings of normal diploid foreskin fibroblasts. They concluded that hTERT (and thus telomerase) preferentially acts on shorter telomeres until uniformity is reached among all telomeres [58]. According to Zhao et al. once telomere size is uniform, telomerase is evenly active on all telomeres [59]. Though telomerase is present in various cells and tissues, its activity is different once those cells and tissues are differentiated during the lifespan of the individual. Also, telomerase acts differently on chromosomes with varying telomere lengths. Therefore, when conducting age estimation studies targeting specific chromosomes based on their attrition rate, it is important to continue to research and understand telomerase expression and activity in tissues and cell types that are most forensically relevant, such as sperm cells and epithelial cells. Also, the behavior of telomeres (telomere attrition and proliferation) must be understood when telomerase is actively present in those cells. In addition, it is important to develop an assay designed to target chromosomes that are more susceptible to telomerase activity in order to reduce overall variability in the results.

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2.2. Variables impacting telomere shortening There are several factors that are being studied for their contribution to telomere shortening over time and are beyond the scope of this review. However, some factors are more prevalent and are the focus of many studies. For example, several studies reported that females have longer telomeres than males due to a faster attrition rate in males, even though the length is the same for both genders at birth [5,52,60–62]. Nawrot et al. concluded that the differences in gender were due to estrogen-responsive hormone regulators in hTERT, causing telomerase to be more active in females, as well as less production and faster metabolization of oxidative species which are associated with telomere shortening. Furthermore, studies of telomere length in leukocytes have indicated that telomere length is paternally inherited by offspring [60,63]. Nawrot et al. found that the correlation between fathers and daughters (when measured before the age of 10) was stronger (r = 0.60) than the correlation between mothers and daughters (r = 0.59) and mothers and sons (r = 0.41). Similarly, Nijajou et al. noted that the correlation was stronger between the father and offspring (measured at birth) (r = 0.46) than mother and offspring (r = 0.18). Also, it was found that paternal age (at conception), but not maternal age, affects telomere length of the offspring at birth [52,64]. In both studies, the offspring had longer telomeres if the father was older in age at conception [52,64], which is likely due to the fact that telomerase is active in sperm cells [55,56]. There are many other studies that explore conditions that have an effect on telomere shortening. The majority of primary publications that explore telomere shortening due to physiological conditions and pathological disease point to oxidative stress which, according to researchers, has been found to accelerate telomere attrition and has been linked to psychological disorders, diabetes, inflammation, vascular diseases, and stress related conditions seen in different ethnicities [11,19–23,65]. In a study of Type 2 Diabetes (T2D) in three different ethnic groups (Caucasian, South Asian, and Afro-Caribbean), Salpea et al. found that individuals with T2D had significantly shorter leukocyte telomere lengths than the control subjects. They were able to conclude that telomere length is associated with T2D which is attributed to high levels of oxidative stress in T2D patients [23]. In a different study by Simon et al., chronic oxidative stress was determined to be the cause of accelerated telomere shortening in patients with mood disorders [21]. Oxidative stress has also been linked to heart disease, and Brouilette et al. reported that when donors who went on to develop coronary heart disease and those who did not were measured, individuals with shorter telomere lengths indeed had a higher risk of developing the disease. The researchers noticed that individuals with shorter telomere lengths had a higher risk of developing the disease. Therefore, they concluded that shorter telomeres are not caused by coronary heart disease, but are a predicting factor of heart disease due to their role in cell senescence [19]. Despite the fact that many physiological conditions and pathological diseases have been found to cause telomere shortening, the direct mechanisms for these effects remain unknown and there are many conflicting studies. For example, several studies have found no differences in telomere length or telomere attrition rate between individuals who were identified as smokers, ex-smokers, and non-smokers, even though smoking is considered a risk factor for inflammation, oxidative stress, and many age-related diseases [62,66]. 3. Additional methods for age estimation While telomere length has been most commonly measured using Southern blot and qPCR techniques [24,25], another method has emerged in the last few years to measure the average length of

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telomeric repeats, ‘‘Q-FISH’’. This method requires the use of quantitative fluorescent in situ hybridization (Q-FISH) of RNA probes labeled with the telomere repeat sequence [67]. The fluorescence can then be analyzed using flow cytometry equipment (flow FISH) [68]. Automation allows this method to be fast, and it is sensitive for a variety of cells found in human blood, such as granulocytes, T cells, and B cells [28]. However, when analyzing small amounts of sample, the fluorescence is more variable and the results are not very accurate or reproducible. The constant need to calibrate the controls to fit the fluorescence signal on a linear scale makes this technique more time consuming and technically demanding, taking 12 h over 2–3 days to complete analysis for a relatively small sample group (approximately 20 samples) [67]. 3.1. Non-telomere based methods Apart from direct measures used to study telomere attrition rate, there have been five additional biomarkers that have been well studied and used for age estimation, one of which includes the measurement of the 4977 bp deletion of mitochondrial DNA [11]. However, studies that tested a variety of human tissues did not discover a direct correlation with age due to the heterogeneity in the abundance of mitochondrial deletions [11,69]. Also, researchers stated that results are not reliable for individuals of advanced age or those with heart disease likely due to the levels of oxidative stress in the patients [70]. A second method that has been explored for age estimation is a method used to measure the age-dependent accumulation of advanced glycation endproducts (AGEs), found in nearly all tissues. AGEs result from non-enzymatic reaction of reducing sugars with amino groups found in proteins, nucleic acids, and lipids [71]. This method has been used for age-estimation studies but poses the same problems as the previously mentioned method [11,72]. The third method described for potential use in age estimation measures aspartic acid racemization, in which an increase in the Daspartic acid protein is detected when protein modification occurs, due to an increase in age, in proteins containing aspartyl and asparaginyl residues [11,73]. The most ideal target tissue analyzed using this method is tooth dentin, due to the accuracy and reproducibility of results obtained with that tissue [11,74]. A fourth method for use in estimating age was described by Ballantyne. For this method, RNA biomarkers were studied to determine donor age and tissue specificity from blood samples. Four gamma hemoglobin transcripts (HBG1n1, HBG1n2, HBG2n2 and HBG2n3) were discovered to be specific for only newborns ranging in age from a few hours old up to 4 months. It was determined that transcripts HBG1n1 and HBG2n3 were detected in blood from 98% of the 51 newborns tested but was undetected in the 81 non-newborns tested (5 months to 92 years old) [1,75]. The fifth and most recently described method of age estimation involves detection of the products of somatic gene rearrangements found in blood T-cells [76,77]. When cells are invaded by foreign antigens, such as bacteria, viruses, or parasites, intervening sequences in the T-cell receptor (TCR) genes are deleted and they form circular DNA molecules known as signal joint TCR excision circles (sjTRECs) [78]. Since sjTRECs decline in number with an increase of age, Zubakov et al. described a TaqMan assay to measure sjTRECs, which was then used to estimate age. The study tested Tcells from blood samples obtained from 195 healthy individuals and the results showed a strong correlation between age and sjTRECs accumulation (R2 = 0.835) with a 9 year standard error (as shown in Table 1) [76]. In addition, the study divided the individuals into four generational groups spanning 20 years, for categorical age prediction using a model-based prediction approach, and the results were positive with the AUC (area under the curve) values ranging from 0.89 to 0.97 [76,77]. Further, this method

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was deemed robust in a study of degraded age samples and was also shown to be quite sensitive when small DNA inputs (5 ng) from younger individuals were tested [76]. Like with telomere based methods, there are several issues found with other, non-telomere based methods used for age estimation. For example, the mRNA study by Ballantyne is only relevant for age estimation of newborns and thus may not be applicable to most forensic casework, including those involving violent crimes. The methods used for measuring mitochondrial DNA deletions, AGE accumulation, aspartic acid racemization also require further research due to the limited types of tissues that can be tested with these methods and the unknown affects of oxidative stress on these measures [11]. The most promising nontelomere based approach for predicting age is to measure sjTRECs accumulation in T-cells as described by Zubakov et al. However, this method can only be used to test peripheral blood (T-cells) and therefore does not apply to other forensically relevant tissues such as epithelial or sperm cells [76]. Further, the affects of biogeographical ancestry of individuals as well as the affects of physiological and pathological conditions (including immunological diseases) on the reproducibility of results using this method are unknown and could limit practical utility of this method [76]. Very recently, two groups have described genetic sites whose methylation patterns correlate with age in epithelial cells (R = 0.83 and R > 0.6, respectively) [79,80]. However, other tissue types have not been tested nor have the effect of other variables been explored, so the full utility of this method has yet to be determined. In addition to the technical issues with the individual methods described above, some of the aforementioned studies did not control for other variables such as race, gender, or disease [1,6]. Like previously mentioned, Hewakapuge et al. did divide the data from their samples into subgroups based on age, race, and gender for further analysis but were still unable to find a correlation between telomere length and age. However, in this study, when the samples were divided into groups, the sample size decreased significantly (to only 9 and 10 individuals per group), reducing the overall confidence in the results [15]. Similarly, the samples size for the research conducted by Ballantyne was also low because only one individual (n = 1) was chosen for each age group (16 age groups) and not every age was represented in the study [1].

4. Future perspectives In conclusion, it is necessary to design basic science experiments that will better address the deficiencies noted in the previously published literature on the subject of molecular prediction of biological age. Telomeres remain the most promising biomarker for this purpose; mechanisms for telomere repeat attrition over time have been well documented over the course of more than twenty years [81]. Further, telomeres are on all chromosomes, and are known to shorten in all nucleated cells in all tissue types. However, assays currently used to determine telomere length are not precise or reproducible and are reported to be susceptible to a number of other variables that can alter the measure. Thus, before one can rule out the utility of telomere length as a molecular biomarker for age prediction, a single-chromosome targeted technique must be designed in order to confirm the correlation between age and telomere length, and to determine the true strength of this correlation in different population subgroups. The effects of tissue type must also be examined as new techniques are developed, to assure that the method will be viable for forensic purposes. Lastly, new techniques being considered should be robust enough to determine age across a broad spectrum of age ranges and the effect of other variables (gender, race, smoking, disease, etc.) must be explored and corrected for.

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