Roles of DNA methylation and hydroxymethylation in aging, immunosenescence, and gene regulation Open Access
Johnson, Nicholas (Spring 2021)
Abstract
DNA methylation (DNAm) refers to the binding of a methyl group to DNA, which typically occurs at a cytosine nucleotide that is directly followed by a guanine nucleotide from 5’ to 3’. DNAm can hold genes in a stably repressed state. It is associated with age, many chronic diseases and biological processes. DNA hydroxymethylation (DNAhm) is a more recent discovery and less well-characterized than DNAm. Unlike DNAm, DNAhm does not bind to known gene repressors (MBD1, MBD2, MBD3) and is associated with cis-gene expression. I first review the role of DNAm and DNAhm in one such biological process known as immunosenescence. Immunosenescence is the general deterioration of the immune system with age, and it is characterized by functional changes in hematopoietic stem cells (HSCs) and specific blood cell types as well as changes in levels of numerous factors, particularly those involved in inflammation. DNAm and DNAhm is not only associated with many immunosenescence-related processes but both of these epigenetic modifications play a causal role in some of them. In addition to reviewing the role of DNAhm and DNAm in immunosenescence, this dissertation specifically investigates age-related DNAhm and its relationship to age-related gene expression. I observed an overrepresentation of directional consistency between age-related DNAhm and age-related gene expression, which contrasts the repressive gene regulatory role of DNAm. This section is followed by an investigation of the capacity of single-cell methylation sequencing to detect novel imprints. Allele-specific methylation (ASM) is the mechanism underlying the establishment of imprints, which regulate gene expression by holding one allele of a gene in a stably repressed state. In this section, I developed a criteria to detect ASM at the sample-level. I then compared ASM detected at the sample-level to the methylation state in individual cells at CpGs with heterozygous sites sufficiently close to the CpGs to distinguish the methylation state of each allele, which constituted a cellular-level gold-standard. In this comparison, ASM detected in ≥3 samples was also ASM in the cellular-level gold standard 95% of the time. These results demonstrate that single cell methylation sequencing is a powerful tool to detect novel imprints.
Table of Contents
TABLE OF CONTENTS
Chapter 1: Introduction p.1
REFERENCES p.6
Chapter 2: The role of DNA methylation and hydroxymethylation on immunosenescence p.11
TABLES p.43
FIGURES p.44
REFERENCES p.47
SUPPLEMENTARY MATERIAL p.60
Chapter 3: Age-related DNA hydroxymethylation is enriched for gene expression and immune system processes in human peripheral blood p.63
TABLES p.84
FIGURES p.86
REFERENCES p.90
SUPPLEMENTARY MATERIAL p.96
Chapter 4: Single cell methylation has the potential to detect novel imprints and distinguish between hemi-methylation and allele-specific methylation
p.125
TABLES p.145
FIGURES p.149
REFERENCES p.156
SUPPLEMENTARY MATERIAL p.158
Chapter 5: Conclusions p.163
About this Dissertation
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Primary PDF
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Roles of DNA methylation and hydroxymethylation in aging, immunosenescence, and gene regulation () | 2021-04-03 17:46:30 -0400 |
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Supplemental Files
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Supplementary Table 3.3 (Genomic information for the 6,650 DhmRs. Column 1 (binIndex) is a unique index for each of the 6,650 bins, column 2 (chr) indicates the chromosome of the bin, column 3 (start) and 4 (end) are the start and end positions of the bin according to the hg38 build of the human genome, column 5 (featureName) is the name of a genomic feature the bin overlaps, column 6 (featureType) indicates the type of overlapping feature (Gene Body, Promoter, or Enhancer), column 7 is the DESeq2 test statistic and column 8 is the corresponding p-value. DhmRs overlapping multiple genomic features have multiple rows, one for each overlapping feature.) | 2021-04-03 17:46:35 -0400 |
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Supplementary Table 3.4 (Genomic information of the 124 DEGs including the gene name (column 1), the Ensembl name (column 2), chromosome (column 3), start (column 4) and stop position (column 5), DESeq2 test statistic (column 6), p-value (column 7), and q-value) | 2021-04-03 17:46:43 -0400 |
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