Abstract
Epigenetics is a rapidly developing field focused on deciphering chemical fingerprints that accumulate on human genomes over time. As the nascent idea of precision medicine expands to encompass epigenetic signatures of diagnostic and prognostic relevance, there is a need for methodologies that provide high-throughput DNA methylation profiling measurements. Here we report a novel quantification methodology for computationally reconstructing site-specific CpG methylation status from next generation sequencing (NGS) data using methyl-sensitive restriction endonucleases (MSRE). An integrated pipeline efficiently incorporates raw NGS metrics into a statistical discrimination platform to identify functional linkages between shifts in epigenetic DNA methylation and disease phenotypes in samples being analyzed. In this pilot proof-of-concept study we quantify and compare DNA methylation in blood serum of individuals with Parkinson's Disease relative to matched healthy blood profiles. Even with a small study of only six samples, a high degree of statistical discrimination was achieved based on CpG methylation profiles between groups, with 1008 statistically different CpG sites (p < 0.0025, after false discovery rate correction). A methylation load calculation was used to assess higher order impacts of methylation shifts on genes and pathways and most notably identified FGF3, FGF8, HTT, KMTA5, MIR8073, and YWHAG as differentially methylated genes with high relevance to Parkinson's Disease and neurodegeneration (based on PubMed literature citations). Of these, KMTA5 is a histone methyl-transferase gene and HTT is Huntington Disease Protein or Huntingtin, for which there are well established neurodegenerative impacts. The future need for precision diagnostics now requires more tools for exploring epigenetic processes that may be linked to cellular dysfunction and subsequent disease progression.
Highlights
Non-infectious diseases were once considered to be the result of faulty genes
In this paper we present a pilot feasibility study for the development of a high-resolution DNA methylation profiling technology based on computational reconstruction of the probabilities of cytosine methylation states from generation sequencing (NGS) data
We have developed a computational approach to harness the power of methyl-sensitive restriction enzymes with the base-pair specific precision of next generation sequencing (NGS) to statistically reconstruct CpG site-specific methylation in heterogenous cell population samples (Marsh and Pasqualone, 2014)
Summary
The push of the last 50 years to decode the human genome carried the hope that we would be able to identify gene mutation errors and develop corrective gene therapies. Identifying the associations between diseases and genetic variants has been a major challenge to improving human wellness (Weitzel et al, 2016). Epigenetic modifications of genomes are recognized as playing central interacting roles in genetic determinants of health and disease (Ladd-Acosta and Fallin, 2016). When methylation is present gene activation is suppressed. This gene expression regulation directed by epigenetics in a large part explains why an individual is not a pre-programed reflection of a parentally inherited genome, but instead during their life they can develop alternative paths toward different outcomes of health and wellness
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