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Phase II Reactions: Methylation Reactions

Phase II Reactions: Methylation Reactions

Methylation is a phase II biotransformation process involving the attachment of a methyl group to a substrate. Enzymes known as methyltransferases orchestrate this reaction.
The mechanism of methylation unfolds in two stages. The first stage sees a methyltransferase enzyme facilitating the transfer of a methyl group from S-adenosylmethionine (SAM) to the substrate, forming S-adenosylhomocysteine (SAH). The second stage involves further metabolism of SAH into homocysteine, which can be recycled...

Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

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Updated: Jul 3, 2025

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

A supervised learning method for classifying methylation disorders.

Jesse R Walsh¹, Guangchao Sun¹, Jagadheshwar Balan¹

¹Mayo Clinic, Rochester, MN, USA.

BMC Bioinformatics

|February 12, 2024

Summary

This summary is machine-generated.

This study introduces an advanced method for detecting abnormal DNA methylation patterns by adjusting for age and sex. This approach enhances the accuracy of diagnosing methylation-related disorders using machine learning.

Keywords:

Angelman syndrome Beckwith–Wiedemann syndrome Congenital disease Diagnosis Machine learning Methylation Prader–Willi syndrome Russell–Silver syndrome Silver–Russell syndrome

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Last Updated: Jul 3, 2025

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Area of Science:

Epigenetics
Genomics
Computational Biology

Background:

DNA methylation is a key epigenetic alteration with clinical biomarker potential.
Current diagnostic methods for methylation disorders use fixed cutoffs, ignoring age and sex variations.
These limitations hinder accurate identification of abnormal methylation patterns.

Purpose of the Study:

To develop an age- and sex-adjusted method for genome-wide DNA methylation outlier detection.
To create a machine learning pipeline for classifying methylation-associated congenital disorders.
To improve diagnostic accuracy for epigenetic disorders.

Main Methods:

Genome-wide DNA methylation profiling of a cohort including healthy individuals and patients with specific genetic syndromes.
Application of a Generalized Additive Model for age and sex adjusted outlier analysis across ~700,000 CpG sites.
Development of an ensemble-based machine learning pipeline utilizing z-scores for classification.

Main Results:

Successfully profiled genome-wide DNA methylation in a diverse cohort.
Achieved a high combined prediction accuracy of 0.96 for sample classification.
Demonstrated the effectiveness of age and sex adjustment in outlier detection.

Conclusions:

A novel method for age- and sex-adjusted outlier detection of differentially methylated loci has been established.
A custom machine learning pipeline effectively classifies samples for potential methylation-associated congenital disorders.
The proposed methods achieve high accuracy in identifying abnormal methylation patterns.