Gaining insights into Alzheimer’s disease by predicting chromatin spatial organization

TL;DR

This paper explores how chromatin structure, specifically CTCF loops, may contribute to Alzheimer’s disease by developing a predictive model and analyzing epigenetic disruptions.

Contribution

A novel CTCF loop predictor is developed and applied to Alzheimer’s disease data to uncover new insights into its etiology.

Findings

The CTCF loop predictor achieved F-scores over 0.9 in GM12878 and K562 cell lines.

Top disrupted loops in Alzheimer’s data were previously linked to the disease in literature.

The study highlights new clues about CTCF’s role in Alzheimer’s etiology and development.

Abstract

CTCF is a conserved protein involved in the establishment and maintenance of topologically associating domains (TADs) and loops. Alzheimer’s disease (AD) represents the most common form of dementia, affecting over 50 million elderly individuals. Epigenetic alterations are a hallmark of AD, and epigenetic disruptions are able to affect CTCF binding and looping. Understanding the dynamics of CTCF loops behind AD may lead to new, undiscovered contributions of CTCF to the etiology of AD. To understand the dynamics behind CTCF loops, we developed a CTCF loop predictor using different genomic and epigenomic features, such as CTCF motif information, CTCF protein binding information, and different histone marks.

We obtained F-scores of over 0.9 in GM12878 and K562 cell lines. We reported the importance of each feature in classification, and compared the results with other loop predictors. After testing the predictor, we predicted loops in control and AD data, reported a score of loop disruption and selected the top disrupted loops on AD which were all previously linked with AD in bibliography. Our study contributes to a better understanding of the role of CTCF binding and CTCF loops in gene regulation, and highlights new clues about CTCF in the etiology and development of AD.

The method can be found in https://github.com/networkbiolab/jalpy.

Figures