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BRIDGE-AD lab
课题组长姓名： 王文良助理教授 研究员 博士生导师, PhD, 助理教授
职务： 所在院所： 生命科学与技术学院
荣誉称号： 教育经历：
博士后及工作经历：
课题组简介 研究方向：
阿尔兹海默症的免疫和大脑表观遗传机制，遗传与环境互作
研究内容：
Research Areas 1. Genetics × Environment on AD Progression Using Genetically Diverse Mice The interplay between genetic background and environmental exposures is a major but poorly understood driver of AD risk and progression. We leverage genetically diverse mouse models — including the Collaborative Cross and Diversity Outbred populations — to systematically dissect how genetic variation interacts with environmental factors (e.g., diet, infection, stress) to shape epigenomic landscapes in brain and immune cells during AD progression.
Key questions: How does genetic diversity modulate the epigenomic response to environmental exposures in the context of AD? Which genetic variants sensitize or protect specific brain cell types from AD-associated epigenomic changes? Can we identify shared and divergent gene regulatory programs across genetically diverse AD mouse models?
2. Genetics × Environment on AD Progression in Human Longitudinal Cohorts Translating findings from animal models to humans requires studying real-world gene–environment interactions at scale. We analyze longitudinal human cohort data — integrating genetic, epigenomic, and environmental exposure data — to characterize how G×E interactions contribute to AD risk, timing of onset, and rate of progression at the population level.
Key questions: What environmental exposures interact with AD genetic risk variants to accelerate or delay disease onset? Are there epigenomic biomarkers of G×E interactions that predict cognitive decline? How do immune cell epigenomes reflect cumulative genetic and environmental influences in aging individuals?
3. Mechanism of AD Progression Using Mouse Models and iPSC/Fibroblast-Induced Neurons Understanding the cell-autonomous and non-cell-autonomous mechanisms of AD requires tractable experimental systems. We use AD mouse models alongside iPSC-derived and fibroblast-induced neurons to study how specific genetic risk variants and environmental perturbations rewire gene regulatory networks in disease-relevant cell types.
Key questions: How do AD risk variants alter chromatin organization and gene expression in neurons, glia, and immune cells? What are the epigenomic signatures of early versus late neurodegeneration in model systems? Can induced neuron models faithfully recapitulate the epigenomic features of human AD brain?
4. G×E AI Model for Precision Prevention of AD Integrating multi-modal genomic, epigenomic, and environmental data requires new computational frameworks. We are developing AI models that learn from G×E interaction data to predict individual AD risk trajectories and identify actionable intervention windows for precision prevention.
Key questions: Can we build interpretable AI models that predict AD onset and progression from epigenomic and environmental features? Which G×E interactions are most predictive of disease risk across diverse populations? How can AI-guided epigenomic signatures guide personalized lifestyle or pharmacological interventions? 研究成果展示代表性论文（*第一作者，#通讯作者）
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