Biology subjects

JIANG, W.

Publications and source records attributed to JIANG, W..

3 recordsLinked to original sources

bioRxiv2026

traceCB: Trans-ancestry cell-type-specific eQTLs mapping by integrating scRNA-seq and bulk data

Mapping cell-type-specific expression quantitative trait loci (ct-eQTLs) is essential for interpreting disease-associated variants, yet studies in underrepresented populations are hindered by limited statistical power. Here, we present traceCB, a statistical framework that enhances ct-eQTL mapping in target ancestries by integrating summary statistics from single-cell and bulk-tissue eQTL studies across diverse populations. By explicitly modeling trans-ancestry genetic architecture and accounting for cellular heterogeneity in bulk tissues, traceCB optimizes information borrowing from well-powered European cohorts while robustly controlling for type I error. Simulation studies demonstrate that traceCB achieves superior statistical power compared to original ct-eQTL, particularly when leveraging tissue-level data. In an application to immune cells in East Asian and African cohorts, traceCB increased the effective sample size by up to 2.9-fold and identified approximately 40% more eGenes than single-ancestry analyses, with a replication rate exceeding 90% in independent datasets. Furthermore, traceCB improved the colocalization of regulatory variants with GWAS signals for blood and immune-related traits, revealing cell-type-specific mechanisms underlying complex diseases. These findings establish traceCB as a powerful and scalable tool for leveraging global genomic resources to improve regulatory variant discovery at the cellular level across diverse populations.

genomics↗

bioRxiv2026

A Multi-modal LLM-Knowledge Fusion Framework for Predicting Single-cell Genetic Perturbation Effects

Understanding cellular responses to genetic perturbations is fundamental for drug discovery, yet experimental approaches face significant limitations in coverage and cost that prevent comprehensive mapping of cellular behavior. This has motivated the development of virtual cells--computational models that learn the relationship between cell state and function to predict the consequences of perturbations across diverse contexts. However, current computational methods suffer from limited accuracy in complex genetic interactions, poor biological interpretability, and inadequate generalization to unseen genes, severely constraining virtual cell capabilities. We present scPert, a multi-modal framework based on Transformer architecture that integrates large language model embeddings with structured biological knowledge to predict single-cell transcriptomic responses to genetic perturbations. Through hierarchical fusion of knowledge graph representations, contextual embeddings from foundation models, and gene-specific encodings, scPert achieves significant performance improvements in both single-gene and combinatorial perturbations over existing methods. In cancer-relevant applications, scPert demonstrates the capability to reveal p53 pathway dynamics and immune checkpoint regulatory mechanisms. Systematic evaluation on 42 cancer dependency genes demonstrates scPerts ability to identify critical potential therapeutic targets. Our framework establishes a powerful computational foundation for virtual cell construction and accelerates drug target discovery.

bioinformatics↗

bioRxiv2024

Catalytic-independent functions of INTAC in conferring sensitivity to BET inhibition

Chromatin and transcription regulators are critical to defining cell identity through shaping epigenetic and transcriptional landscapes, with their misregulation being closely linked to oncogenesis. Pharmacologically targeting these regulators, particularly the transcription activating BET proteins, has emerged as a promising approach in cancer therapy, yet intrinsic or acquired resistance frequently occurs with poorly understood mechanisms. Using genome-wide CRISPR screens, we find that BET inhibitor efficacy in mediating transcriptional silencing and growth inhibition depends on the auxiliary module of the INTAC complex, a global regulator of polymerase pause-release dynamics. This process bypasses a requirement for INTACs catalytic activities and instead leverages direct engagement of the auxiliary module with the RACK7/ZMYND8-KDM5C complex to remove histone H3K4 methylation. Targeted degradation of the COMPASS subunit WDR5 to attenuate H3K4 methylation restores sensitivity to BET inhibitors, highlighting how simultaneously targeting coordinated chromatin and transcription regulators can circumvent drug-resistant tumors.

molecular biology↗