Abstract
Although the global prevalence of leprosy has decreased over the last few decades due to an effective multidrug regimen, large numbers of new cases are still being reported, raising questions as to the ability to identify patients likely to spread disease and the effects of chemotherapy on the overall incidence of leprosy. This can partially be attributed to the lack of diagnostic markers for different clinical states of the disease and the consequent implementation of differential, optimal drug therapeutic strategies. Accordingly, comparative bioinformatics and Mycobacterium leprae protein microarrays were applied to investigate whether leprosy patients with different clinical forms of the disease can be categorized based on differential humoral immune response patterns. Evaluation of sera from 20 clinically diagnosed leprosy patients using native protein and recombinant protein microarrays revealed unique disease-specific, humoral reactivity patterns. Statistical analysis of the serological patterns yielded distinct groups that correlated with phenolic glycolipid I reactivity and clinical diagnosis, thus demonstrating that leprosy patients, including those diagnosed with the paucibacillary, tuberculoid form of disease, can be classified based on humoral reactivity to a subset of M. leprae protein antigens produced in recombinant form.