Prospective study of serological conversion as a risk factor for development of leprosy among household contacts

Use of protein microarrays to define the humoral immune response in leprosy patients and identification of disease-state-specific antigenic profiles

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.

ML0405 and ML2331 are antigens of Mycobacterium leprae with potential for diagnosis of leprosy

Despite the success of multidrug therapy in reducing the number of registered leprosy cases worldwide, evidence suggests that Mycobacterium leprae continues to be transmitted. A serological diagnostic test capable of identifying and allowing treatment of early-stage disease could reduce transmission and prevent the onset of the disability, a common complication of the disease in later stages. Serological diagnosis based on antibody recognition of phenolic glycolipid I (PGL-I) cannot reliably identify individuals with lower bacterial indices (BI). One strategy that might improve this situation is the provision of highly specific serological antigens that may be combined with PGL-I to improve the sensitivity of diagnosis. Using serological expression cloning with a serum pool of untreated lepromatous leprosy (LL) patients, we identified 14 strongly reactive M. leprae proteins, 5 of which were previously unstudied. We present results suggesting that two of these proteins, ML0405 and ML2331, demonstrate the ability to specifically identify LL/borderline lepromatous (BL) patients on the basis of immunoglobulin G (IgG) reactivity. In a household contact study, LL index cases were identified on the basis of this reactivity, while household contacts of these patients demonstrated undetectable reactivity. At a serum dilution of 1:800, suitable to reduce background PGL-I IgM reactivity, two BL patients with a BI of <4 showed anti-human polyvalent immunoglobulin G, A, and M reactivity measured with a combination of ML0405, ML2331, and natural disaccharide O-linked human serum albumin (NDOHSA) (synthetic PGL-I) that was markedly higher than IgM reactivity to NDOHSA alone. We suggest that ML0405 and ML2331 may have utility in serological leprosy diagnosis.

Genetic, household and spatial clustering of leprosy on an island in Indonesia: a population-based study

Background

It is generally accepted that genetic factors play a role in susceptibility to both leprosy per se and leprosy type, but only few studies have tempted to quantify this. Estimating the contribution of genetic factors to clustering of leprosy within families is difficult since these persons often share the same environment. The first aim of this study was to test which correlation structure (genetic, household or spatial) gives the best explanation for the distribution of leprosy patients and seropositive persons and second to quantify the role of genetic factors in the occurrence of leprosy and seropositivity.

Methods

The three correlation structures were proposed for population data (n = 560), collected on a geographically isolated island highly endemic for leprosy, to explain the distribution of leprosy per se, leprosy type and persons harbouring Mycobacterium leprae-specific antibodies. Heritability estimates and risk ratios for siblings were calculated to quantify the genetic effect. Leprosy was clinically diagnosed and specific anti-M. leprae antibodies were measured using ELISA.

Results

For leprosy per se in the total population the genetic correlation structure fitted best. In the population with relative stable household status (persons under 21 years and above 39 years) all structures were significant. For multibacillary leprosy (MB) genetic factors seemed more important than for paucibacillary leprosy. Seropositivity could be explained best by the spatial model, but the genetic model was also significant. Heritability was 57% for leprosy per se and 31% for seropositivity.

Conclusion

Genetic factors seem to play an important role in the clustering of patients with a more advanced form of leprosy, and they could explain more than half of the total phenotypic variance.