bioRxivGenomicsPreprint
Epigenomic and functional characterization of a core DNA methyltransferase in the human pathogen Clostridium difficile
Pedro H. OliveiraiD, John W. Ribis, Elizabeth M. Garrett, Dominika Trzilova, Alex Kim, Ognjen SekulovićiD, Edward A. MeadiD, Theodore Pak, Shijia Zhu, Gintaras Deikus, Marie TouchoniD, Colleen Beckford, Nathalie E. ZeitouniiD, Deena Altman, Elizabeth Webster, Irina Oussenko, Supinda BunyavanichiD, Aneel K. Aggarwal, Ali BashiriD, Gopi Patel, Camille Hamula, Shirish Huprikar, Eric E. SchadtiD, Robert SebraiD, Harm van BakeliD, , Gang FangiD
Abstract
Clostridioides difficile is a leading cause of health care-associated infections. Although significant progress has been made in the understanding of its genome, the epigenome of C. difficile and its functional impact has not been systematically explored. Here, we performed the first comprehensive DNA methylome analysis of C. difficile using 36 human isolates and observed great epigenomic diversity. We discovered an orphan DNA methyltransferase with a well-defined specificity whose corresponding gene is highly conserved across our dataset and in all ~300 global C. difficile genomes examined. Inactivation of the methyltransferase gene negatively impacted sporulation, a key step in C. difficile disease transmission, consistently supported by multi-omics data, genetic experiments, and a mouse colonization model. Further experimental and transcriptomic analysis also suggested that epigenetic regulation is associated with cell length, biofilm formation, and host colonization. These findings open up a new epigenetic dimension to characterize medically relevant biological processes in this critical pathogen. This work also provides a set of methods for comparative epigenomics and integrative analysis, which we expect to be broadly applicable to bacterial epigenomics studies.
- organism: C. elegans
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: statistical model
- approach: experimental assay
- Citations
- 2
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