Del Moral-Morales, A. | Search bioRxiv

bioRxiv2025

Identification of enhancer chromatin signatures involved in dopaminergic induction through multi-omics analysis

The establishment of precise transcriptional programs during neuronal development depends on a complex and dynamic landscape of regulatory elements. Here, we combined histone modification ChIP-seq, chromatin accessibility profiling, and transcriptomics to map active enhancer repertoires during the differentiation of human embryonic stem cells into midbrain dopaminergic neurons. Our integrative analysis revealed thousands of stage-specific enhancers, over half of which were previously unannotated, and uncovered coordinated chromatin and transcriptional transitions linking pluripotency exit to neuronal lineage commitment. Functional enrichment of enhancer-linked target genes delineated distinct regulatory programs, pluripotency maintenance in undifferentiated cells and midbrain specification in differentiated neuron, while transcription factor motif analysis identified regulatory modules, including a dopaminergic-specific RFX4 network. We further characterized enhancer-gene relationships showing concordant changes in chromatin state and expression for neuronal genes, suggesting enhancer-driven control of dopaminergic identity. These findings provide a genome-scale framework for interpreting how dynamic enhancer landscapes encode developmental fate decisions in the human nervous system.

genomics↗

bioRxiv2024

The power and limits of predicting exon-exon interactions using protein 3D structures

Alternative splicing (AS) effects on cellular functions can be captured by studying changes in the underlying protein-protein interactions (PPIs). Because AS results in the gain or loss of exons, existing methods for predicting AS-related PPI changes utilize known PPI interfacing exon-exon interactions (EEIs), which only cover [~]5% of known human PPIs. Hence, there is a need to extend the existing limited EEI knowledge to advance the functional understanding of AS. In this study, we explore whether existing computational PPI interface prediction (PPIIP) methods, originally designed to predict residue-residue interactions (RRIs), can be used to predict EEIs. We evaluate three recent state-of-the-art PPIIP methods for the RRI- as well as EEI-prediction tasks using known protein complex structures, covering [~]230,000 RRIs and [~]27,000 EEIs. Our results provide the first evidence that existing PPIIP methods can be extended for the EEI prediction task, showing F-score, precision, and recall performances of up to [~]38%, [~]63%, and [~]28%, respectively, with a false discovery rate of less than 5%. Our study provides insights into the power and limits of existing PPIIP methods to predict EEIs, thus guiding future developments of computational methods for the EEI prediction task. We provide streamlined computational pipelines integrating each of the three considered PPIIP methods for the EEI prediction task to be utilized by the scientific community.

bioinformatics↗