A gene promoter is a region of DNA which acts like a switch to “turn a gene on”. The more methylated the promoter is, the more it is “turned on”.

RNA is a key molecule found in cells responsible for a variety of biochemical processes that are essential to the integrity of the cell (D’Aquila, 2017). Hypomethylation (reduced levels of methylation) of the RNA gene promoter has been observed in many different types of cancer (Ghoshal K, 2004). Additionally, hypermethylation (high levels of methylation) of the RNA promoter gene has also been associated with Alzheimer’s disease (Pietrzak M, 2011).

Within ageing research, there is growing interest in the RNA promoter and it’s methylation status. This is because of it’s association with age related disease (D’Aquila, 2017).

What Is DNA methylation?

DNA Methylation is used to control gene expression and maintain stability in the genome. Methylation refers to the addition of a methyl group via a covalent bond at the fifth carbon on a cytosine base within a CpG dinucleotide giving rise to 5-methyl cytosine (Jin, 2011). A CpG dinucleotide is a site where a cytosine base lies next to a Guanine base in the DNA sequence connected via a phosphodiester bond (Jr, 2017). Hypermethylation of CpG sites is often associated with transcriptional repression, conversely, hypomethylation is associated with transcriptional activation (Bird, 1992). DNA methylation prevents the binding of transcription factors to the DNA or leading to transcriptional silencing (Bird, 2001). Special enzymatic molecules called DNA Methyltransferases (DMNT1, DMNT3a and DMNT3b) catalyse DNA methylation. This is outlined in figure 1.1 (Cheng, 2008).

Figure 1.1: DNA Cytosine ring showing the process in which the DNA methyltransferase enzyme (DNMT) facilitates methylation on the Carbon 5 position. Figure Taken from (Cheng, 2008).

Why Is DNA Methylation So Important?

DNA methylation occurs throughout life and is essential for normal development beginning during embryonic development. DNA methylation is carried out in two different forms. Maintenance methylation and de novo methylation. The most common enzyme within maintenance methylation is DNA methyltransferase 1 (DNMT1) which is used to methylate hemi-methylated CpG dinucleotides in the genome, ensuring reformation of parental DNA methylation pattern which can be lost in the daughter DNA (Chen T, 2006).Within de novo methylation however, the DNMT3 family of catalytic enzymes are present. These are DNMT3a and DNMT3b which can newly methylate cytosine groups. Predominantly occurring within the embryo development stages of a mammal’s life cycle. There is also another enzyme present within the DNMT3 family known as DNMT3L which is largely inactive but has been observed to stimulate methylation of DNA by DNMT3a when they are both co-expressed (D’Aquila, 2017). The difference between de novo and maintenance methylation is illustrated below in figure 1.2.

Figure 1.2: De novo methylation vs maintenance methylation. The pale blue segments illustrate substrate sequences (mainly CpG sites) whilst the turquoise shapes represent methyl groups on cytosines. After replication or repair the duplex is methylated on a single strand only. Figure taken from (Cheng, 2008).

References

Åsa Johansson, S. E. (2013). Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan. PLOS ONE, e67378.

Andrew E. Teschendorff, U. M.-M. (2010). Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Research, 440-446.

Armstrong NJ, M. K. (2017). Aging, exceptional longevity and comparisons of the Hannum and Horvath epigenetic clocks. Epigenomics, 9, 689-700.

Bartlett, Z. (2014, November 14). The Hayflick Limit. Retrieved from The Embryo Project Encyclopedia: https://embryo.asu.edu/pages/hayflick-limit

Berdyshev GD, K. G. (1967). Nucleotide composition of DNA and RNA from somatic tissues of humpback and its changes during spawning. Biokhimiya, 32, 988-993.

Bilian Jin, Y. L. (2011). DNA Methylation: Superior or Subordinate in the Epigenetic Hierarchy? Genes & Cancer, 607–617.

Bird. (1992). The essentials of DNA methylation. Cell 70, 5-8.

Bird, A. (2001). Methylation talk between histones and DNA. Science , 2113–2115.

Bjornsson HT, S. M. (2008). Intra-individual change over time in DNA methylation with familial clustering. The Journal of the American Medical Association, 299, 2877-2883.

Bocklandt S, L. W. (2011). Epigenetic predictor of age. PLOS One, 6, e14821.

Casillas MA Jr, L. N. (2003). Transcriptional control of the DNA methyltransferases is altered in aging and neoplastically-transformed human fibroblasts. Molecular and Cellular Biochemistry, 252, 33-43.

Chao Sheng, J. J. (2018). A stably self-renewing adult blood-derived induced neural stem cell exhibiting patternability and epigenetic rejuvenation. Nature Communications 9, Article number: 4047.

Chen T, L. E. (2006). Establishment and maintenance of DNA methylation patterns in mammals. Current Topics in Microbiology and Immunology, 179-201.

D’Aquila, P. (2017). Methylation of the ribosomal RNA gene promoter is associated with aging and age related decline. Aging Works, 966-975.

Feinberg AP, I. R. (2010). Personalized epigenomic signatures that are stable over time and covary with body mass index. Science Translational Medicine, 2, 49ra67.

Field AE, R. N. (2018). DNA Methylation Clocks in Aging: Categories, Causes, and Consequences. Molecular Cell, 882-895.

Fraga MF, B. E. (2005). Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences of the United States of America, 102, 10604-10609.

Ghoshal K, M. S. (2004). Role of human ribosomal RNA (rRNA) promoter methylation and of methyl-CpG-binding protein MBD2 in the suppression of rRNA gene expression. Journal of Biological Chemistry, 6783–6793.

Hannum G, G. J. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell, 359-367.

Horvath S, a. R. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19, 371–384.

Horvath S, P. C. (2015). Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging, 1159–70.

Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, R115.

Issa JP, O. Y. (1994). Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nature Genetics, 7, 536-540.

J.Catania, D. (1991). DNA methylation and cellular ageing. Mutation Research/DNAging, 283-293 .

Jr, W. C. (2017). Medical Definition of CpG . Retrieved from Medicine Net: https://www.medicinenet.com/script/main/art.asp?articlekey=26443

Kalyan K. Pasumarthy, N. D. (2017). Methylome Analysis of Human Bone Marrow MSCs Reveals Extensive Age- and Culture-Induced Changes at Distal Regulatory Elements. Stem Cell Reports, 999–1015.

Kananen L, M. S. (2016). The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: evidence from two longitudinal studies. Age, 3, Article number: 65.

Kang GH, L. S. (2003). Profile of aberrant CpG island methylation along the multistep pathway of gastric carcinogenesis. Laboratory investigation; a journal of technical methods and pathology, 83, 635-641.

Karen A. Lillycrop, S. P. (2013). DNA methylation, ageing and the influence of early life nutrition . Proceedings of the Nutrition Society, 413–421.

Lisa M. McEwen, A. M. (2017). Differential DNA methylation and lymphocyte proportions in a Costa Rican high longevity region. Epigenetics & Chromatin, 10, Article number: 21.

Loukas Zagkos, M. M. (2019). Mathematical models of DNA methylation dynamics: Implications for health and ageing. Journal of Theoretical Biology, 184-193.

Maegawa, S. (2017). Caloric restriction delays age-related methylation drift. Nature Communications, 539.

Mendelsohn, L. (2017). Epigenetic Drift Is a Determinant of Mammalian Lifespan. Rejuvenation Research, 430-436.

Pappas, S. (2018, January 30). Weird: Naked Mole Rats Don’t Die of Old Age. Retrieved from Live Science: https://www.livescience.com/61568-naked-mole-rats-no-aging.html

Peter D. Fransquet, J. W. (2019). The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clinical Epigenetics, 11:62.

Pietrzak M, R. G. (2011). Epigenetic silencing of nucleolar rRNA genes in Alzheimer’s disease. PLoS ONE, e22585.

Sonia Shah, A. F. (2014). Genetic and environmental exposures constrain epigenetic drift over the human life course. Genome Research, 1725-1733.

Singhal RP, M.-H. L. (1987). DNA methylation in aging of mice. Mechanisms of Ageing and Development, 41, 199–210.

Sun, D. (2014). Epigenomic Profiling of Young and Aged HSCs Reveals Concerted Changes during Aging that Reinforce Self-Renewal. Cell Stem Cell, 673-688.

Talens RP, B. D. (2010). 45. Talens RP, Boomsma DI, Tobi EW et al. (2010) Variation, patterns, and temporal stability of DNA methylation: considerations for epigenetic epidemiology. The FASEB Journal, 24, 3135-3144.

Xiaodong Cheng, a. R. (2008). Mammalian DNA Methyltransferases: A Structural Perspective. Structure, 341-350.

Hassan Ahmed

Hassan graduated with a Master’s degree in Chemical Engineering from the University of Chester (UK). He currently works as a design engineering consultant for one of the largest engineering firms in the world along with being an associate member of the Institute of Chemical Engineers (IChemE).

The post The Role of DNA Methylation Within an RNA Gene Promoter | Introduction | Part 1 appeared first on Engineeringness.

Age Related Changes to DNA methylation

Changes in the levels of DNA methylation during ageing have been reported since the1960s. Subsequent studies since then involving different species of mammalians such as rats, mice and human subjects have all shown substantial evidence to support the hypothesis of a progressive loss in levels of DNA methylation during ageing (Singhal RP, 1987; Bollati V, 2009). The expression of the enzymes involved in the process of DNA methylation, DNA methyltransferase (DNMT), have also been reported to change during ageing (Lillycrop, 2013). This is supported by Casillas et al. (2003) who reported that the levels of DNA methyltransferase 1 (DNMT1) and DNA methyltransferase 3a (DNMT3a) declines during the ageing. On the other hand, the levels of DNMT3b increases. The increase in global hypomethylation is supported by these findings as DNMT1 is the maintenance DNMT enzyme (Casillas MA Jr, 2003). Alongside a decrease in global hypomethylation there is also an increase in gene-specific promoter hypermethylation. This is  supported by genes that become hypermethylated with age such as the oestrogen receptor (Issa JP, 1994), myogenic differentiation 1 (JP, 2000) and E-cadherin (Kang GH, 2003). Recently Lowe et al 2020.  studied DNA methylation “clocks” in naked mole rats as a predictor of ageing. The naked mole rat is the longest-lived rodent (Pappas, 2018). Changes in DNA methylation at 51 CpG sites were analysed from their livers (age 39-144 weeks). Changes in methylation in 23 CpG sites were significantly associated with age. This work supports previous findings that there is a correlation between changes in DNA methylation and increased age in mammals.

Epigenetic drift refers to the errors in maintenance of the epigenome, which is responsible for controlling cell identity and function. This is elegantly summarised by Shah et al. (2014) as the divergence of the epigenome as a function of age due to stochastic changes in methylation (Shah, 2014). This is a hallmark of ageing (Mendelsohn, 2017). To this end Maegawa et al. (2017), measured and analysed the differences between average methylation between three different species: Young mice, Rhesus monkeys and Humans. The study found that the average methylation increased from 2 to 18, 2 to 22 and 3 to 20 within Young mice, Rhesus monkeys and Humans respectively within unmethylated sites compared to the same sites at an older age. It was also found that drift rates were calculated as 4.1%, 0.34% and 0.1% per year for Young mice, Rhesus monkeys and Humans respectively, establishing an inverse relationship between the rate of methylation drift and life expectancy in these species.

Another important age-related factor is the change in expression of DNA methylation and demethylation enzymes. Sun et al. (2014) discovered the expressions for genes encoding for DNMT, DNMT1, DNMT3a and DNMT3b were drastically reduced between the ages of 4 and 24 months within C57BL/6 male mice (Sun, 2014).

DNA methylation changes have been investigated in several longitudinal studies. Feinberg et al. (2010), carried out a genome wide screen over 11 years investigating a sample size of 4 million CpG sites. This produced a result of 227 regions, showing extreme variably methylated regions across the genome, falling into two categories of dynamic and stable methylation (Feinberg AP, 2010). Bjornsson et al. (2008) measured the levels of global DNA methylation in whole blood in two separate investigations. The first was conducted over 11 years and examined whole blood from 111 individuals (59-86 years). The second study was conducted over 16 years and examined whole blood from 127 individuals (5 -72 years). Results revealed 8-10% of individuals studied showed >20% change in methylation over the 11-16-years. Additionally, the changes showed familial clustering of both increased and decreased methylation. Family members did not live in the same household during the study, suggesting  genotype influences the rate of change of methylation with age (Bjornsson HT, 2008).

Monozygotic twins have been studied in a bid to discount the effect of genetic background on epigenetic ageing. Fraga et al. (2005) compared the DNA methylation patterns in a pair of 3 year old and 50 year old monozygotic twins and found that the distribution of DNA methylation marks were distinct for the 50 year old monozygotic twins but showed significant overlap of the genomic distribution of the 5-methylcytosine of the 3 year old monozygotic twins (Fraga MF, 2005). A study by Talens et al. (2010) who investigated 460 individuals which comprised 230 monozygotic twin pairs (aged between 18-89 years old). It was found the variation in global DNA methylation increased proportionally with age. This supports the study by Fraga et al. (2005). Mean methylation and variance was also examined for several genes associated with common diseases over a range of candidate ages. Mean methylation did not differ significantly between the young and old ages. Conversely, there was an increase in variation of DNA methylation with age. There was a significant difference in the stability of different CpG sites, the most substantial age related variation increase in DNA methylation appearing at CpG sites linked to metabolic and homeostatic related genes such as IGF-2 and leptin (Talens RP, 2010). DNA methylation from saliva samples of 34 pairs of identical twins (aged between 21-55 years old) was analysed and the results found 88 CpG sites near 80 genes where DNA methylation was substantially correlated with age (Bocklandt S, 2011). Currently it is assumed  that one third of the CpG sites reveal age associated DNA methylation changes, of which 60% become hypomethylated and 40% become hypermethylated during ageing (Johansson, 2013). Age associated DNA methylation is related across different tissues. However, it is different depending on the cell type (Teschendorff, 2010).

Epigenetic Clocks

Early studies have eluded to a “clock” underlying ageing (J.Catania, 1991). Conducted in the early 1990’s Catania et al. (1991) suggested that a continuous loss in DNA methylation levels in cell cultures is associated or connected to the number of cell divisions that occur during a cell’s lifetime. Catania et al. (1991), considered that a progressive loss of DNA methylation produces a multi-step cell division “clock” which underlies the Hayflick phenomenon (J.Catania, 1991).  According to Hovarth et al. (2013), the epigenetic clock can be described as a prediction method of age based on the linear combination of the DNA methylation levels of 353 CpG dinucleotides (Horvath, 2013). This work provides more evidence that the methylation landscape changes dramatically with ageing.

Biological Versus Chronological Age

Several studies have been conducted to establish whether a participants age’ is biologically different to their chronological age by measuring age acceleration. These studies have either used the Hovarth epigenetic clock which uses 353 CpG dinucleotides or the hannum clock which uses 71 CpG dinucleotides (Fransquet, 2019). Hovarth et al. (2015) found that 55 year olds have a lower DNA methylation age compared to their chronological age and that their offspring have a lower age acceleration and intrinsic age acceleration compared to the controls (Horvath S P. C., 2015). Mcewan et al. (2017) measured Nicoyans from the Nicoya peninsula of Costa Rica where they have one of the highest old age life expectancies in the world. They concluded that there were no age acceleration differences between Nicoyans and age matched controls (McEwen, 2017). Three separate cohorts were analysed and investigated in another study which found DNA methylation age was highly correlated with chronological age (r=0.93). However, they found the correlation was lower in each cohort separately (r= 0.52-0.73) (Armstrong NJ, 2017). Kananen et al. (2016) found that DNA methylation age was moderately correlated with chronological age over the course of 25 years (r=0.54). it also found that younger participants aged faster than older participants (Kananen L, 2016). Epigenetic age predictions do not only correlate with chronological age but are also indicatory for life expectancies (Field AE, 2018).

Mathematical Modelling of DNA Methylation

Mathematical modelling is defined by Southern et al. (2008), as an abstract representation of a complex system in mathematical form (Southern, 2008). Most models used to describe complex biological systems require simplifying assumptions, therefore, the solution of the model becomes an approximation of the original biological system, thus, can be used to provide an understanding into the elements that influence the nature of more complex systems. The accuracy and precision of a model is dependent on the validity of the assumptions, usually the accuracy and precisions of a model are enhanced by making model building an iterative process (Southern, 2008).

Computational modelling has become the cornerstone of biological studies and are routinely conducted alongside laboratory experiments. The computational modelling process essentially involves inputting the mathematics into a computational system to simulate the dynamic behaviour of a biological system under investigation. There are several advantages to computational modelling over traditional approaches. These are:

1. a hypothesis can easily be explored by a model,
2. a model can be easily updated as the biology of ageing updates,
3. experiments can be run quickly and efficiently,
4. a model provides a framework for brining disparate biological data together.

Due to its ability to represent the complexities of DNA methylation mathematical modelling has become a tool that has been applied widely in this area. Moreover, due to several factors such as its cost effectiveness, ease of use and efficiency, it overcomes many of the obstacles faced by conventional methods, facilitating the quantitative analysis of complex biochemical systems.

Previous Models of DNA Methylation

One of the first mathematical models of DNA methylation dynamics was developed by Otto et al. (1990) who presented a model for the kinetics of methylation and demethylation of eukaryotic DNA. The model incorporated de novo methylation and an error rate for maintenance methylation. The equation used in the model presented a steady state equilibrium where the rate of newly methylated sites is the same as the rate at which site are demethylated within cell generation. The equations developed by Otto et al. (1990) can be used to analyse the whole genome or specific regions within the genome (Otto, 1990). Kinetic models have also been used to model DNA methylation. An advantage of kinetic modelling. Kinetic models take into account several interlinked enzyme catalysed reaction rates (Sauer, 2010). Unfortunately, a disadvantage of kinetic modelling is they require kinetic parameter data to build them (J. Schaber, 2009). Other approaches have used Markov chains. A Markov chain is defined as a set of transitions, which are determined by a probability distribution which satisfies the Markov property. A Markov property refers to the memoryless property meaning that a given probability distribution is independent of its history (Soni, 2018). An advantage of the Markov model is that it is an analytical method therefore the reliability parameters are calculated using a mathematical formula leading to higher speed and accuracy associated with them (Egerton, 2016). A disadvantage however, is that due to the Markov model being memoryless, if in practice the assumption of memoryless is not completely applicable to the system and how it functions in practice, the accuracy of the model is lessened (Egerton, 2016). Capra et al. (2014) modelled DNA methylation dynamics using approaches from phylogenetics using this approach to combat the lack of models which consider the dependency of precursor and dependant cells. A continuous time Markov chain approach was utilised and tested by the analysis of high-resolution methylation map of mouse stem cells into numerous blood cells, the model achieved a 90% correct score. The model was capable of ascertaining unobserved CpG methylation states from observed methylation states from the same sites in related cell types (Capra, 2014).

Another Markov chain model innovated by Sontag et al. (2006) describes the development of hypo- to hypermethylated equilibria from methylation noise in a fixed number of CpG sites. Sontag et al’s model explains the persistent coexistence of the two equilibria of hypo- and hypermethylated sites, sporadic changes of site-specific methylation levels that may lead to an altering of pre-set epigenetic imprints in renewing cell populations (Sontag, 2006). Przybilla et al. (2014) further expanded on Sontag et al by using a computational approach using the same model of DNA used by Sontag et al (2006) to understand epigenetic changes in ageing within stem cells. Przybilla et al. (2014) combined an individual cell-based model of stem cell populations with a model of epigenetic regulation of transcription enabling a simulation of age related trimethylation of lysine 4 at histone H3 and of DNA methylation. The findings showed that epigenetic ageing intensely affects stem cell heterogeneity and that homing of stem cells niches slows down epigenetic ageing (Przybilla, 2014).

Genereux et al. (2005), developed a probabilistic model of DNA methylation dynamics to better understand how methylated and unmethylated states of Cytosine are transferred during DNA replication. A probabilistic model accounts for randomness, in the form of random variables, which in turn affects model outcomes (Stephanie,2017). This inherent random variable assumption is seen as an advantage. Another advantage is that a probabilistic model often provides a realsiic representation of the biology at a molecular level (S. Bouwman, 2005). A disadvantage is the longer calculation times compared to other modelling techniques (S. Bouwman, 2005). The model was designed to track methylated, hemimethylated and unmethylated CpG/ CpG dyads at a CpG site over several cells from a single tissue within a single individual. The model can infer site-specific rates of de novo methylation and maintenance methylation (Genereux, 2005).

Another probabilistic model representing two kinds of stochasticity was developed by Flottmann et al. (2012). It described the interplay between gene expression, chromatin modifications and DNA methylation. The model deduces cytosine methylation rates at several sites within the Human promoter gene FMR1. However, there were variations in results after extensive analysis showing room for optimization of the process. Faster changes in DNA methylation increasing the speed of the reprogramming at the cost of efficiency whilst enhanced chromatin modifications somewhat improve efficiency (Flöttmann, 2012).

DNA methylation in most tissues and cell lines is stochastically moderately variable with each site reliant on site-specific probabilities (Varley, 2013). A stochastic model is defined as a model which possess randomness embedded within the model (Stephanie, 2016). Riggs and Xiong. (2004) developed a stochastic methylation model in which for each CpG dyad there is a evident efficiency of both maintenance and de novo methylation (Xiong, 2004). Building further on the work done by Riggs and Xiong. (2004), Jeltsch and Jurkowska. (2014) included the rate of cell division in conjunction with active and passive demethylation. This was done by using a stochastic equation model with rates of cell division as well as rates of active and passive demethylation (Jeltsch, 2014). Rannala et al. (2001) utilised a stochastic model to analyse the methylation patterns and stem cell turnovers within the human colon. The findings suggested human crypts are long-lived whilst accumulating random errors in methylation and compromise of stem cells which undergo “bottlenecks” during life. Stem cell bottlenecks are described as events which lead to the drastic reduction of the population size. The remaining population after the bottleneck undergo significant genetic drift (Scitable, population bottleneck, 2014). The stochastic model simulated crypts with a methylation error rate of 2 x 10^-5  per CpG site per division, with one division occurring per day (Rannala, 2001). Haerter et al. (2014) used a stochastic simulation of CpG islands. Furthermore, Haerter et al. (2014) found that a dynamic collaboration between CpG’s can provide reliable error tolerant somatic inheritance of both hypermethylated and hypomethylated states of multiple CpGs, this reproduced witnessed, stable bimodal methylation patterns. Inheritance was predicted as being strengthened by the recruitment of demethylating enzymes by unmethylated CpGs, allowing CpG islands to remain hypomethylated within an expanse of hypermethylation (Haerter, 2014). Two years later Olariu et al. (2016) expanded on the work done by Haerter and used the same stochastic model to model the regulatory network of Oct4, Tet1 and Nanog including positive feedback loops comprising of DNA demethylation across the promoters of Oct4 and Tet1. A mechanistic understanding of regulatory dynamics was put forward which outlined that DNA methylation is the key to regulation of pluripotent genes. Most recently, Olariu et al. (2016) provided a template for a novel framework connecting transcription regulation with DNA methylation models (Olariu, 2016).

Partial differential equations were used by McGovern et al. (2012) to produce a dynamic three – compartmental model of DNA methylation based on the activity of DNMT methyltransferase proteins which are as follows: unmethylated CpG dyads, hemimethylated CpG dyads and methylated CpG dyads. These are portrayed in figure 2.1.

Figure 2.1: Three distinct states of a CpG dyad: unmethylated (x₁), hemimethylated (x₂) and methylated (x₃). An unmethylated CpG site is portrayed as a white circle contrasting to the black circle which depicts a methylated CpG site. An unmethylated CpG dyad has both of the CpG sites opposite one another unmethylated (x₁), A hemimethylated CpG dyad has only one of the CpG sites opposite methylated and one unmethylated (x₂) and a methylated CpG dyad has both of the CpG sites opposite one another methylated (x₃). Figure taken from (Zagkos, 2019).

The model also includes the Tet proteins, which enzymatically transform methycytosine to hydorxymethylcytosine. The DNMT1, DNMT3a and DNMT3b as well as DNA replication are all responsible for the transition states between the CpG dyads. Partial differential equations used in the model as shown in figure 1.2 show the methylation rates of unmethylated and hemimethylated CpG dyads denoted by k₁ and k₂ respectively. Moreover, the demethylation rates denoted by k₃ and k₄ represent hemimethylated and methylated CpG dyads respectively. D denotes the rate of cell division see figure 2.2. The model provided an accurate representation of DNA methylation dynamics (McGovern, 2012).

Figure 2.2: A diagram to show the methylation rates between unmethylated (x₁), hemimethylated (x₂) and unmethylated (x₃). Figure taken from (Zagkos, 2019).

In 2013 Zheng et al. (2013) developed a CpGIMethPred, a support vector machine based model which predicts the methylation status of CpG islands within a human genome under normal conditions. The parameters used for prediction within the model are ones that have been previously demonstrated as effective. These included DNA sequence composition patterns, DNA structure patterns, CpG island specific attributes, distribution patterns of conserved transcription factor binding sites and conserved elements and methylation status. The three-part model can be used to gain a CpG island map. This compromises of database extraction of methylation data, feature extraction and statistical predictive modelling (Zheng, 2013). Most recently, a spatial stochastic model has been used by Lück et al. (2020) to model single CpGs and generalize them into multiple CpGs using SAN (stochastic automata network). The results of this model were verified against extensive results from Monte Carlo simulations (Wolf, 2020).

In 2019 Zagkos et al. (2019) developed upon the model created by McGovern et al. (2012).

Summary Table

References

Åsa Johansson, S. E. (2013). Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan. PLOS ONE, e67378.

Albert Jeltsch, R. Z. (2014). New concepts in DNA methylation. Trends in Biochemical Sciences, 310-318.

Andrew E. Teschendorff, U. M.-M. (2010). Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Research, 440-446.

Andrew P. McGovern, B. E. (2012). A dynamic multi-compartmental model of DNA methylation with demonstrable predictive value in hematological malignancies. Journal of Theoretical Biology, 14-20.

Armstrong NJ, M. K. (2017). Aging, exceptional longevity and comparisons of the Hannum and Horvath epigenetic clocks. Epigenomics, 9, 689-700.

Bjornsson HT, S. M. (2008). Intra-individual change over time in DNA methylation with familial clustering. The Journal of the American Medical Association, 299, 2877-2883.

Bocklandt S, L. W. (2011). Epigenetic predictor of age. PLOS One, 6, e14821.

Bollati V, S. J. (2009). Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mechanisms of Ageing and Development, 130, 234-239.

Casillas MA Jr, L. N. (2003). Transcriptional control of the DNA methyltransferases is altered in aging and neoplastically-transformed human fibroblasts. Molecular and Cellular Biochemistry, 252, 33-43.

Diane P. Genereux, B. E. (2005). A population-epigenetic model to infer site-specific methylation rates from double-stranded DNA methylation patterns. Proceedings of the National Academy of Sciences of the United States of America, 5802-5807.

Egerton, A. (2016). Markov Analysis – A Brief Introduction. Retrieved from egerton consulting: https://egertonconsulting.com/markov-analysis-brief-introduction/?doing_wp_cron=1587499122.9402499198913574218750

Feinberg AP, I. R. (2010). Personalized epigenomic signatures that are stable over time and covary with body mass index. Science Translational Medicine, 2, 49ra67.

Field AE, R. N. (2018). DNA Methylation Clocks in Aging: Categories, Causes, and Consequences. Molecular Cell, 882-895.

Fraga MF, B. E. (2005). Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences of the United States of America, 102, 10604-10609.

Issa JP, O. Y. (1994). Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nature Genetics, 7, 536-540.

Hao Zheng, H. W. (2013). CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome. BMC Medical Genomics, Article number: S13.

Horvath S, a. R. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19, 371–384.

Horvath S, P. C. (2015). Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging, 1159–70.

Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, R115.

J. Schaber, W. L. (2009). Nested uncertainties in biochemical models. IET Systems Biology, 1-9.

J.Catania, D. (1991). DNA methylation and cellular ageing. Mutation Research/DNAging, 283-293.

Jan O. Haerter, C. L. (2014). Collaboration between CpG sites is needed for stable somatic inheritance of DNA methylation states. Nucleic Acids Research, 2235–2244.

Jens Przybilla, T. R. (2014). Understanding epigenetic changes in aging stem cells – a computational model approach. Aging Cell, 16-18.

John A. Capra, D. K. (2014). Modeling DNA methylation dynamics with approaches from phylogenetics. BIOINFORMATICS, i408–i414.

JP, A. N. (2000). Aging, methylation and cancer. Histology & Histopathology, 835-842.

Kananen L, M. S. (2016). The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: evidence from two longitudinal studies. Age, 3, Article number: 65.

Kang GH, L. S. (2003). Profile of aberrant CpG island methylation along the multistep pathway of gastric carcinogenesis. Laboratory investigation; a journal of technical methods and pathology, 83, 635-641.

Karen A. Lillycrop, S. P. (2013). DNA methylation, ageing and the influence of early life nutrition . Proceedings of the Nutrition Society, 413–421.

Katherine E. Varley, J. G. (2013). Dynamic DNA methylation across diverse human cell lines and tissues. Genome Research, 555–567.

Laura B. Sontag, M. C. (2006). Dynamics, stability and inheritance of somatic DNA methylation imprints. Journal of Theoretical Biology, 890-899.

Lisa M. McEwen, A. M. (2017). Differential DNA methylation and lymphocyte proportions in a Costa Rican high longevity region. Epigenetics & Chromatin, 10, Article number: 21.

Loukas Zagkos, M. M. (2019). Mathematical models of DNA methylation dynamics: Implications for health and ageing. Journal of Theoretical Biology, 184-193.

Maegawa, S. (2017). Caloric restriction delays age-related methylation drift. Nature Communications, 539.

Max Flöttmann, T. S. (2012). A stochastic model of epigenetic dynamics in somatic cell reprogramming. Frontiers in Physiology, Article no: 216.

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Hassan Ahmed

Hassan graduated with a Master’s degree in Chemical Engineering from the University of Chester (UK). He currently works as a design engineering consultant for one of the largest engineering firms in the world along with being an associate member of the Institute of Chemical Engineers (IChemE).

The post The Role Of DNA Methylation Within an RNA Gene Promoter | Literature Review | Part 2 appeared first on Engineeringness.

The electron microscope has two key advantages when compared to light microscopes: it has a much higher range of magnification (can detect smaller structures) and it has a much higher resolution (can provide clearer and more detailed images). Electron microscopy (EM) is an extremely powerful tool for investigating the minute structure of biological materials. It can be used for determining biological structures at the molecular level, such as proteins and organelles such as the nucleus or mitochondria etc.

It gives a three-dimensional image of the sample, which is far more representative of the real sample than a two-dimensional image. Unlike a light microscope, an electron microscope is able to see details below human vision, which is limited to about 200 nanometers, and create an image of the sample that can usually be magnified a million times or more.

How Does This Magnification Work?

The electrons are focused by passing them through a magnetic lens, a device which is quite similar to the optical lens in a light microscope. The electrons follow a spiral path because it takes more force to change the path of a moving electron than a stationary electron. Most of the electrons go to the outer rim of the lens, but a few go to the centre. These are the ones that can be made to do the work of focusing the beam. The amount that the beam is focused is controlled by the voltage used to accelerate the electrons. Since the electric field of the electrons is aligned with the magnetic field, the electrons loop so that their electric field is always parallel to the magnetic field. The exact relationship between the magnetic field and the electric field is called the Larmor equation.

How Do You Magnify The Image?

After the sample is focused, you must magnify the image. This is controlled by the voltage difference applied between the lens and the sample. The electrons collect on the sample material and transfer energy to it, causing the electrons in the sample material to change to higher energy levels. The electrons do not stay in lower energy levels for long because they lose energy as they move around in the material. Because the material is in a vacuum, the electrons emit photons (light) as they lose energy. New electrons fill the lower energy level, but now they are at a higher energy level, which causes them to emit light of a different wavelength. (The wavelength, or colour, for the light, depends on which element is being studied; for example, researchers can tell which element is in an element by the difference between light emitted by electrons that move along the edge and electrons that move along the face of the sample. )

A device similar to a television screen separates the colours of light based on their wavelengths. The colours are captured by a camera and effects the electron beam in a way that makes it appear as though the source has a slightly different position than the original image. The copies of the image are made one at a time and can be magnified to higher levels. The light is going directly from the electron to the photocathode of the camera, so when it hits the photocathode, it excites a tiny electron that jumps off the surface. After it leaves the surface, it may hit another surface and lose more energy, and then it may hit the photocathode again and excite a second electron. If the electron loses too much energy, it has nothing left to excite the photocathode and form an image.

To avoid these problems, the electron microscope uses a high accelerating voltage (up to 300,000 volts) and a very small electron beam. The microscope counts the electrons passing through a small hole, then compares the number counted with the number that the lens would count if it were a light microscope. The difference between the two counts is the exact magnification of the image.

How Is A Bright-field Image Obtained?

The easiest way to understand how a bright-field image is obtained is to understand how a dark-field image is obtained. A bright-field image is obtained by focusing the electrons on the sample. A dark-field image is obtained by focusing the electrons around the sample. Because electrons scatter easily from objects that are nearby, this approach gives images in which the sample appears to be a hole in a black background. Dark-field images tend to be grainy because the unscattered electrons tend to be reflected off the boundaries of the sample without much variation of intensity. Because a bright-field image has more variation in intensity, it is usually a better choice for examining a biological sample.

Hassan Ahmed

Hassan graduated with a Master’s degree in Chemical Engineering from the University of Chester (UK). He currently works as a design engineering consultant for one of the largest engineering firms in the world along with being an associate member of the Institute of Chemical Engineers (IChemE).

The post A Breakdown | What Is The Advantage Of An Electron Microscope? appeared first on Engineeringness.

Direct protein determination is when protein content is calculated based on the analysis of amino acid residues. Indirect protein determination can for instance be inferred following the determination of the nitrogen content or after chemical reactions with functional groups within the protein (Mæhre, 2018).

The amount of protein, both total and component protein, in food can be determined using various analytical methods in a laboratory. In animal feeds, it is essential to have accurate determinations of protein content. The major reason is that protein is a major nutrient and, as a result of new EU legislation, the nutrient content of individual feed production batches must be determined. Typical methods used at the laboratory are described below.

Direct Protein Determination

These are methods used for determining the amino acid profile in a food sample. It is then assumed that amino acids make up proteins. Using an amino acid-specific pattern of methylation, a direct calculation can then be made of the total protein content. An example would be when analysing hydrolysed soya protein, the amino acid profile is 98% Digestible amino acids used at the laboratory for analysing the protein content of soya protein are:

L-lysine, L-methionine, L-phenylalanine, L-tryptophan, L-threonine, L-isoleucine, L-leucine, L-valine, L-alanine, L-arginine, L-glutamine, L-histidine, L-proline, L-serine and L-tyrosine.

Other direct protein determination methods are based on the analysis of individual amino acids present. A reference range for each analyte determines a specific protein content. This method is used for vegetable proteins and isolated protein components e.g. in powders.

Indirect Protein Determination

Levels of total nitrogen and protein can be determined following the combustion of proteins (i.e. combustion of the carbon present in the sample in the presence of oxygen, typically N 2 is mixed with the sample before combustion). The amount of nitrogen produced is therefore used as a measure of the protein content.

The National Amino Acid Pattern (NAAAP) in feed protein is seen as a more accurate method for protein determination compared to nitrogen. This is because the amino acid analysis includes the determination of glutamic and glutamine, which are a part of the total nitrogen present.

Chromatography is a very useful method for protein determination. It can be used either for the total protein, or individual amino acids present in a food sample. If the sample is mixed with an organic solution in a hexagonal plate and an electric current is passed through the mixture, the polar protein will move towards the cathode during the operation cycle. Other non-polar compounds will “drop off” at various times during the cycle, for instance, fat falls off within the first minute.

Protein content in feed can be calculated from the analysis of the different component amino acids present. However, there is no general protein pattern for feeds. This requires a reference material that contains known amino acid sequences.

References

Hanne K. Mæhre, *. L.-J. (2018). Protein Determination—Method Matters. NCBI.

Hassan Ahmed

Hassan graduated with a Master’s degree in Chemical Engineering from the University of Chester (UK). He currently works as a design engineering consultant for one of the largest engineering firms in the world along with being an associate member of the Institute of Chemical Engineers (IChemE).

The post A Breakdown | What Is Protein Determination? appeared first on Engineeringness.