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Human Gut Phageome Analysis Uncovers Thousands of Highly Modular Endolysins

Niloy, R. K.; Jewel, N. A.; Karim, D.; Rolin, M. H.; Khan, T.; Akter, A.; Mondal, S. I.

2026-04-28 genomics
10.64898/2026.04.25.720813 bioRxiv
Show abstract

The escalating threat of antimicrobial resistance has renewed global interest in bacteriophages as precise and powerful tools for controlling bacterial populations in the human gut. These viruses owe much of their antibacterial potential to phage-encoded endolysins, enzymes capable of rapidly degrading bacterial cell walls with high specificity and low potential for resistance development. Despite their therapeutic promise, the overall composition of the gut phageome and the structural modularity of its endolysins remain poorly understood. In this study, we performed a large-scale analysis of 9,141 human gut metagenomic samples from 34 independent studies. Using standardized workflows for assembly, genome clustering, host prediction, and protein domain annotation, we reconstructed 15,267 phage genomes and identified 3,794 corresponding endolysins. The recovered genomes showed substantial variation in size and coding density, with an average GC content of 43%. Host prediction indicated that most phages targeted bacterial members of the phyla Bacillota (41%) and Bacteroidota (23%). Endolysin sequences grouped into 296 protein families and displayed striking domain modularity. Catalytic domains such as Amidase_2 and Glyco_hydro_25 frequently co-occurred with cell wall-binding motifs including LysM and CW_7. Remarkably, one endolysin contained 15 distinct domains, the highest natural domain diversity reported to date. Collectively, this study represents the most comprehensive characterization of the human gut phageome and its encoded endolysins to date. The exceptional modular diversity uncovered highlights the gut phageome as a rich reservoir of endolysin variants, providing a strong foundation for developing next-generation therapeutics against multidrug-resistant bacterial pathogens.

Published in MicrobiologyOpen · training set

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