Dissecting sequence determinants of DNA methylation and in silico perturbation

Authors:
  • FCFaming Chen
    Faming Chen
  • QZQinhao Zhang
    Qinhao Zhang
  • SZShiwei Zhu
    Shiwei Zhu
  • JLJinxia Liang
    Jinxia Liang
  • SZSiyun Zhong
    Siyun Zhong
  • ZZZehong Zhang
    Zehong Zhang
  • JSJianzhong Su
    Jianzhong Su
  • YGYangfan Guo
    Yangfan Guo
  • YZYan Zhang
    Yan Zhang
  • FYFulong Yu
    Fulong Yu
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Abstract
DNA methylation shapes cellular identity and genome function, yet the sequence logic that governs its establishment remains largely unresolved. Here we present MethylAI, a cross-species–pretrained and human-specialized deep learning framework that predicts single-CpG methylation states directly from genomic sequence with high fidelity. Through quantitative attribution, MethylAI uncovers intrinsic cis-regulatory principles embedded in DNA, revealing conserved transcription factor (TF) motifs whose influence extends beyond CpG composition. Activated motifs demarcate gene-body CpG islands, encode lineage-defining methylation states, and align with TF occupancy and diverse histone modifications, thereby linking sequence syntax to local chromatin regulation. CTCF perturbation experiments validated MethylAI-predicted methylation shifts at activated motifs, showing strong concordance with in silico perturbations. Mapping genetic variants onto methylation-linked active motifs further uncovered widespread connections to zinc-finger TF families, providing mechanistic insight into how noncoding variation contributes to human traits and diseases. By transforming DNA sequence into interpretable methylation grammar, MethylAI establishes a generalizable framework for decoding the regulatory architecture of the human epigenome.
Funding Information
  • National Key R&D Program of China (2023YFF1204701)
  • Guangdong Basic and Applied Basic Research Foundation (2024B1515020080)
  • National Natural Science Foundation of China (32470634)
  • National Natural Science Foundation of China (32460155)
  • Talent Research Funding Project of Guangdong Provincial People’s Hospital (KY012023362)
  • Major Project of Guangzhou National Laboratory (GZNL2024A01003)
  • Major Project of Guangzhou National Laboratory (GZNL2023A02002)
  • Key R&D Program of Yunnan (202302AD080004)
  • Key R&D Program of Yunnan (202403AC100002)
  • Clinical Medical Center for Cardiovascular Disease of Yunnan Province (2024YNLCYXZX0195)

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