Abstract
During recent years, comparative genomic analysis has allowed the identification of Mycobacterium leprae-specific genes with potential application for the diagnosis of leprosy. In a previous study, 58 synthetic peptides derived from these sequences were tested for their ability to induce production of IFN-γ in PBMC from endemic controls (EC) with unknown exposure to M. leprae, household contacts of leprosy patients and patients, indicating the potential of these synthetic peptides for the diagnosis of sub- or preclinical forms of leprosy. In the present study, the patterns of IFN-γ release of the individuals exposed or non-exposed to M. leprae were compared using an Artificial Neural Network algorithm, and the most promising M. leprae peptides for the identification of exposed people were selected. This subset of M. leprae-specific peptides allowed the differentiation of groups of individuals from sites hyperendemic for leprosy versus those from areas with lower level detection rates. A progressive reduction in the IFN-γ levels in response to the peptides was seen when contacts of multibacillary (MB) patients were compared to other less exposed groups, suggesting a down modulation of IFN-γ production with an increase in bacillary load or exposure to M. leprae. The data generated indicate that an IFN-γ assay based on these peptides applied individually or as a pool can be used as a new tool for predicting the magnitude of M. leprae transmission in a given population.