Biology subjects

SUN, Q.

Publications and source records attributed to SUN, Q..

2 recordsLinked to original sources

bioRxiv2026

A Promoter Competition Hub Orchestrates Runx1 Alternative Promoter Usage during Skeletal Muscle Stem Cell Activation

Alternative promoter (AP) usage profoundly expands transcriptomic and proteomic diversity, yet the regulatory principles governing promoter choice remain poorly understood. Here, using the dual-promoter Runx1 locus as a paradigm in skeletal muscle stem cells (MuSCs), we uncover a promoter competition hub that orchestrates AP selection during MuSC fate transition from activation to proliferation. We find antagonistic expression dynamics between the two Runx1 promoters, with the primary promoter (PP) active in the early activating stage and the secondary promoter (SP) induced later in proliferation. Combinatorial genetic perturbations in vitro and in vivo establish the non-redundant roles of PP- and SP-derived isoforms in MuSC lineage progression. Mechanistically, we find that PP and SP engage in reciprocal competition in a multi-connected enhancer-promoter (E-P) loop hub. Further dissection identifies a cohort of key enhancers that dynamically interact with PP/SP to orchestrate the competition. Moreover, we establish the transcription factor USF1 as a key factor driving the dynamic E-P interactions and PP/SP competition. Beyond Runx1, high-resolution Micro-C reveals promoter competition as a general mechanism regulating a subset of AP choices in MuSC activation/proliferation. Collectively, our study uncovers a dynamic promoter competition hub that governs AP selection in MuSC lineage progression, offering fundamental insights into how 3D genome architecture coordinates precise, stage-specific gene expression programs during cell fate transitions.

genetics↗

bioRxiv2026

caRBP-Pred: Deep Learning-based Prediction of Chromatin-Associated RNA-Binding Proteins Using Short Peptide Sequences

RNA-binding proteins (RBPs) are pivotal in cellular processes ranging from RNA metabolism to 3D genome organization. A distinct subset, chromatin-associated RBPs (caRBPs), binds directly to chromatin to function as transcriptional regulators. However, identifying caRBPs via traditional methods like Chromatin Immunoprecipitation Sequencing (ChIP-seq) and Mass Spectrometry (MS) is labor-intensive and costly. While computational tools for DNA- and RNA-binding protein (DRBP) prediction exist, they often rely on outdated Gene Ontology annotations and fail to capture the unique characteristics of chromatin association. Here, we introduce caRBP-Pred, a novel deep learning approach combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory networks (BiLSTM). Unlike previous methods utilizing full-length sequences, our model is trained on chromatin-contact peptides derived from mouse embryonic stem cells (mESCs). caRBP-Pred achieves a superior Area Under the Curve (AUC) of 0.81 using peptide sequence information alone, significantly outperforming existing DRBP predictors which exhibit low recall for caRBPs. We further predicted 52 potential caRBPs in mice. Notably, validation against human homologs confirmed that our model accurately predicts candidates with experimentally verified chromatin-binding capabilities. Collectively, caRBP-Pred is the first tool specifically designed to predict caRBPs based on chromatin-contact peptides, offering a valuable resource for investigating regulatory roles of caRBPs on transcription.

bioinformatics↗