OR29

1:Nottingham Trent University; 2:Institute of Tropical Medicine, Antwerp

Mycobacterium leprae is the primary cause of leprosy, a chronic infection of the peripheral nerves, causing numbness, muscle weakness, and skin lesions. Each year, 200,000 new cases are reported, predominantly concentrated in a few high-burden countries. Global eradication of leprosy relies on detecting transmission clusters to inform targeted interventions. Molecular epidemiology of M. leprae is in its infancy as this bacterium is unculturable, hindering whole genome sequencing efforts. Recent advances in DNA extraction and sequencing have overcome some of these challenges, creating a need for a robust molecular epidemiology framework to enable detection of transmission clusters based on sequence variation.

This project aims to address this challenge by estimating the effectiveness of both a reference-based mapping approach (using the Snippy pipeline) and a reference-free distance estimation (using SKA-2) to approximate the SNP distances between clinical isolates. We use a set of four complete genomes to first estimate the precision of each approach for estimating SNP distances compared to a ground truth estimated by Minimap2 whole genome alignments. We find that both approaches have similar accuracy for estimating SNP distances with almost no false positives and F1 scores over 0.9. This high accuracy is important as the exceptionally low diversity observed in M. leprae requires high certainty in each SNP call.

Our findings indicate that a robust clinical pipeline for calling SNPs and reconstructing transmission clusters is possible for M. leprae, allowing for molecular epidemiology approaches to be incorporated into leprosy control efforts.