UCLA

DNA methylation, the addition of a methyl group at the fifth carbon of the pyrimidine ring resulting in 5-methylcytosine (5MC), is influenced by the cellular environment; continued exposure to environmental stimuli will result in detectable DNA methylation changes. These detectable changes have been leveraged to develop DNA methylation based predictive models for age and health. DNA methylation is commonly assayed through the use of bisulfite conversion, where unmethylated cytosine is deaminated to form uracial while 5MC is unchanged, followed by high throughput sequencing. Processing of bisulfite sequencing data is computationally demanding due to the asymmetrical nature of bisulifte sequencing data. Chapter 1 introduces a bisulfite sequencing processing platform, BSBolt, that is a signficant improvement over previous tools in terms of processing time and alignment accuracy. BSBolt and targeted bisulfite sequencing are utilized in chapter 2 to look into epigenetic suppression of transgenic t-cell receptor (TCR) expression in adoptive cell transfer therapy. The chapter 2 study shows that accumulation of DNA methylation over the viral vector promoter used to introduce the TCR sequence is associated with decreased expression despite persistence of the TCR sequence over time. Chapter 3 introduces an evolutionary framework, the epigenetic pacemaker (EPM), for modeling epigenetic aging. The EPM is a departure from the penalized regression based approaches broadly used in the field. The EPM attempts to minimize error across the observed methylation profiles rather than age prediction error. This approach allows the EPM to model nonlinear epigenetic aging across human lifespan as shown in chapter 4. Chapter 5 compares the EPM to penalized regression approaches and shows the EPM is more sensitive for detecting biological signals associated with epigenetic aging.