journal article·2026-07-13Open access

A Pan‐Methylome Framework for Population‐Scale Bacterial Epigenomics

Bin Ma, Huimin Gong, Ran Zhuo, Zaixiao Rao, Yanxi Wan, Mingyang Wang, Zahra Zeinalzadeh, Yuan Gao, Aohan Guan, Xinrun Li, Wanting Chen, Xiliang Wang, Yun Wan, Sihua Zhang, Yuncai Xiao, Qi Huang, Paul R. Langford, Xiangru Wang, Rui Zhou, Rui Luo, Hongbo Zhou, Chengliang Zhu, Hui Jin

Abstract

ABSTRACT Epigenetic modifications furnish a hidden regulatory layer that shapes cellular fate and evolutionary potential without rewriting the genetic code. In eukaryotes, methylome maps have profoundly transformed our understanding of development, disease, and lineage diversification. In bacteria, which represent the most abundant and ecologically versatile forms of life, the epigenome remains a largely uncharted dimension. To enable population‐scale comparison of bacterial methylomes, we develop a principled strategy to prioritize methylation systems that are enzymatically encoded, broadly conserved and phylogenetically informative, thereby defining a stable substrate for quantitative comparative analysis. We reconstruct the first population‐scale bacterial methylation‐informed phylogenies that broadly recapitulate sequence‐based relationships while resolving epi‐phylogroups associated with GC content and environmental stress resilience. We introduce a three‐metric quantitative framework (MPK, MR, and MFR) that converts site‐level methylation calls into standardized, cross‐sample comparable quantitative traits, enabling robust identification of highly methylated core genes from as few as 30 Escherichia coli strains. Finally, co‐methylation network analysis identifies virulence‐enriched modules and coordinated methylation of horizontally acquired virulence loci, providing the first evidence that horizontally acquired virulence loci are epigenetically assimilated into host regulatory circuitry. Together, this framework enables locus‐resolved, population‐scale integration of bacterial methylation data to interrogate epigenetic contributions to evolution, fitness and pathogenicity.
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Research context

OpenAlex topics: Bacterial Genetics and Biotechnology · Genomics and Phylogenetic Studies · Machine Learning in Bioinformatics

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Affiliations

State Key Laboratory of Agricultural Microbiology College of Veterinary Medicine Huazhong Agricultural University Wuhan China

Key Laboratory of Preventive Veterinary Medicine in Hubei Province The Cooperative Innovation Center for Sustainable Pig Production Wuhan China

Hubei Jiangxia Laboratory Wuhan China

Department of Biological Sciences Smith College Northampton MA USA

Wuhan Animal Disease Control Center Wuhan Hubei China

Section of Paediatric Infectious Disease Imperial College London South Kensington London UK

Department of Clinical Laboratory Renmin Hospital of Wuhan University Wuhan China

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China

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