Shotgun metagenomic analysis of the oral microbiomes of children with noma reveals a novel disease-associated organism

Abstract

Noma is a rapidly progressive orofacial gangrene that predominantly affects children living in extreme poverty. Despite its documentation since antiquity and its designation as a World Health Organisation Neglected Tropical Disease in 2023, the microbiological cause of noma remains poorly understood, with no specific organisms confidently identified as definitive aetiological agents. Here, we present the first deep shotgun metagenomic profiling of oral saliva microbiomes from 19 Nigerian children with acute noma. Our analyses reveal marked microbial dysbiosis in noma microbiomes, with machine learning and multivariate statistical analyses indicating significant enrichment of Treponema, Porphyromonas, and Bacteroides, alongside depletion of Streptococcus and Rothia, as key microbial signatures of noma disease. From the dataset we recovered 40 high-quality Treponema metagenome-assembled genomes (MAGs) spanning 19 species, 14 of which were novel. Notably, a novel species designated Treponema sp. A was detected in 15 of the 19 noma participants and was entirely absent from global healthy saliva metagenomes. Re-analysis of previously published 16S rRNA datasets from children with noma in Niger also revealed Treponema sp. A to be highly prevalent in noma cases but rare in controls. This study identifies Treponema--particularly Treponema sp. A--as a strong candidate organism associated with noma pathogenesis. Additionally, analysis of antimicrobial resistance determinants detected in noma metagenomes revealed concerning levels of resistance to antibiotics commonly used in noma treatment, particularly {beta}-lactams and metronidazole, especially among Prevotella species. These findings provide the first high-resolution microbial framework for noma and offer a foundation for future research into its pathogenesis and the development of novel diagnostics, therapeutics, and preventive strategies in endemic settings.