Dataset Information

Erythroid lineage chromatin accessibility maps facilitate identification and validation of NFIX as a fetal hemoglobin repressor.

ABSTRACT: Human genetics has validated de-repression of fetal gamma globin (HBG) in adult erythroblasts as a powerful therapeutic paradigm in diseases involving defective adult beta globin (HBB)¹. To identify factors involved in the switch from HBG to HBB expression, we performed Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq)² on sorted erythroid lineage cells derived from bone marrow (BM) or cord blood (CB), representing adult and fetal states, respectively. BM to CB cell ATAC-seq profile comparisons revealed genome-wide enrichment of NFI DNA binding motifs and increased NFIX promoter chromatin accessibility, suggesting that NFIX may repress HBG. NFIX knockdown in BM cells increased HBG mRNA and fetal hemoglobin (HbF) protein levels, coincident with increased chromatin accessibility and decreased DNA methylation at the HBG promoter. Conversely, overexpression of NFIX in CB cells reduced HbF levels. Identification and validation of NFIX as a new target for HbF activation has implications in the development of therapeutics for hemoglobinopathies.

SUBMITTER: Chaand M

PROVIDER: S-EPMC10267139 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Erythroid lineage chromatin accessibility maps facilitate identification and validation of NFIX as a fetal hemoglobin repressor.

Chaand Mudit M Fiore Chris C Johnston Brian B D'Ippolito Anthony A Moon Diane H DH Carulli John P JP Shearstone Jeffrey R JR

Communications biology 20230614 1

Human genetics has validated de-repression of fetal gamma globin (HBG) in adult erythroblasts as a powerful therapeutic paradigm in diseases involving defective adult beta globin (HBB)<sup>1</sup>. To identify factors involved in the switch from HBG to HBB expression, we performed Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq)<sup>2</sup> on sorted erythroid lineage cells derived from bone marrow (BM) or cord blood (CB), representing adult and fetal states, ...[more]

PMID: 37316562

Similar Datasets

Project description:BackgroundAssigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow.ResultsWe validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS.ConclusionsOur work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states.

Dataset Information

Erythroid lineage chromatin accessibility maps facilitate identification and validation of NFIX as a fetal hemoglobin repressor.

Publications

Erythroid lineage chromatin accessibility maps facilitate identification and validation of NFIX as a fetal hemoglobin repressor.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets