Characterizing Chromatin Changes Upon Inhibition of Chromatin Remodeling Complexes Público

Abstract

BRG1/BRM Associated Factors (BAF) complexes are ATP-dependent chromatin remodelers which control chromatin accessibility genome-wide. Mutations in BAF subunits can cause neurodevelopmental disease and cancer. While BAF complexes are known to regulate chromatin accessibility, the specific mechanisms by which they target genomic regions, and the downstream effects of their inhibition remain incompletely understood. In particular, it is unclear which transcription factors or chromatin features determine a region’s sensitivity to BAF activity. Addressing this gap is critical for interpreting how mutations in BAF subunits contribute to disease. Motivated by this, my thesis research aimed to systematically characterize the chromatin-level consequences of BAF inhibition and identify the molecular features predictive of such changes. To do this, I inhibited BAF activity using a small molecule targeting the ATPase subunit of the complex. Using ATAC-seq to profile chromatin accessibility, we observed widespread loss of accessible chromatin regions upon BAF inhibition. Machine learning models, including a random forest classifier and ridge regression, were then trained to predict accessible chromatin sensitive or insensitive to BAF inhibition based on transcription factor binding and histone modification profiles. A random forest classifier achieved accuracies above 78% with high AUROC values, while feature importance analyses from linear regression models highlights distinct roles for promoter-associated factors, CTCF/cohesin subunits and lineage-specific transcription factors (e.g., RUNX3, BATF, JUNB, SPI1) in understanding chromatin response to BAF inhibition. Analysis of known protein-protein interactions in StringDB indicates that transcription factors which bind to BAF subunits are predictive of chromatin accessibility loss upon BAF inhibition, suggesting that these TFs may function to recruit BAF complexes to chromatin via protein-protein interactions. Overall, this work establishes an analytical framework for fundamentally understanding the effects of BAF activity on chromatin.

Table of Contents

1. Abstract, 1

2. Introduction, 2

2.1. The packaging of Eukaryotic DNA into chromatin, 2

2.2. Chromatin accessibility and transcriptional regulation, 2

2.3. The BRG1/BRM Associated Factors (BAF) Complexes control genome-wide chromatin accessibility, 3

2.4. Transcription Factors as Potential Recruiters of BAF, 4

2.5. GM12878 as a system to study chromatin dynamics upon BAF inhibition, 5

2.6. Accessible chromatin regions display heterogeneous responses to BAF inhibition, 5

3. Methods, 6

3.1. Cell culture of GM12878, 7

3.2. BAF-ATPase inhibition of GM12878 cells, 7

3.3. Assay for Transposase Accessible Chromatin with Sequencing (ATAC-seq), 8

3.4. Library preparation and sequencing, 8

3.5. ATAC-seq data processing, 8

3.6. ENCODE BED file parsing and download, 9

3.7. Implementation of machine learning algorithms, 9

3.7.1. Feature Matrix Generation, 9

3.7.2. Random Forest Classifier, 10

3.7.3. Ridge Linear Regression, 10

3.7.4. Evaluation Metrics, 11

3.8. StringDB to predict BAF-Protein Interaction, 12

4. Results, 12

4.1. Treatment of BRM014 to GM12878 cells results in genome wide loss in accessibility, 12

4.2. Machine learning predicts BAF sensitivity of open chromatin at high performance,13

4.3. Mean Decrease in Impurity (MDI) index highlight important features in ML prediction, 15

4.4. Linear regression helps distinguish features that predict BAF sensitivity vs insensitivity, 15

4.5. BAF-sensitivity predicting features are enriched for BAF interaction, 17

5. Discussion, 18

5.1. Machine learning can predict the loss of chromatin accessibility upon BAF inhibition based on protein binding and histone modification, 18

5.2. CTCF/cohesin and promoter-associated modifications are predictive of chromatin accessibly retention, 18

5.3. Lineage Determining and Enhancer associated TFs are predictive of chromatin accessibility loss, 19

5.4. TFs that predict BAF sensitivity point to potential mechanisms of BAF recruitment to chromatin, 19

5.5. Limitations and Future directions, 19

6. References, 21

About this Honors Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Emory College
Department	Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Computer Science Biology, Bioinformatics Biology, Genetics
Palabra Clave	Chromatin BRG1/BRM Associated Factors Complexes
Committee Chair / Thesis Advisor	David Gorkin, Emory University
Committee Members	Arri Eisen, Emory University Yana Bromberg, Emory University

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