Explaining Observed Infection and Antibody Age-Profiles in Populations with Urogenital Schistosomiasis

Abstract

Urogenital schistosomiasis is a tropical disease infecting more than 100 million people in sub-Saharan Africa. Individuals in endemic areas endure repeated infections with long-lived schistosome worms, and also encounter larval and egg stages of the life cycle. Protective immunity against infection develops slowly with age. Distinctive age-related patterns of infection and specific antibody responses are seen in endemic areas, including an infection ‘peak shift’ and a switch in the antibody types produced. Deterministic models describing changing levels of infection and antibody with age in homogeneously exposed populations were developed to identify the key mechanisms underlying the antibody switch, and to test two theories for the slow development of protective immunity: that (i) exposure to dying (long-lived) worms, or (ii) experience of a threshold level of antigen, is necessary to stimulate protective antibody. Different model structures were explored, including alternative stages of the life cycle as the main antigenic source and the principal target of protective antibody, different worm survival distributions, antigen thresholds and immune cross-regulation. Models were identified which could reproduce patterns of infection and antibody consistent with field data. Models with dying worms as the main source of protective antigen could reproduce all of these patterns, but so could some models with other continually-encountered life stages acting as the principal antigen source. An antigen threshold enhanced the ability of the model to replicate these patterns, but was not essential for it to do so. Models including either non-exponential worm survival or cross-regulation were more likely to be able to reproduce field patterns, but neither of these was absolutely required. The combination of life cycle stage stimulating, and targeted by, antibody was found to be critical in determining whether models could successfully reproduce patterns in the data, and a number of combinations were excluded as being inconsistent with field data.

Protective immunity to Schistosoma haematobium infection is primarily an anti-fecundity response stimulated by the death of adult worms

“…T cells are assumed to contribute to the generation and maintenance of the antibody response but are not explicitly included. The model was based upon a framework used in earlier deterministic population-level models (16), with worm survival assumed to be approximately Gaussian distributed. The model structures used were those that met a panel of criteria over the greatest region of parameter space …”

“…Antibody aggregation was also assessed but was not included as a formal criterion, because the range of levels obtained from field data was too broad to be informative. The data and methods used to draw up the criteria for the peaked age intensity curve, reduced infection level in adults, the peak shift, the antibody switch, and the age of the antibody switch have been previously described (16). Prevalence and aggregation of S. haematobium infection were calculated from data from six Zimbabwean communities (14,44,45) for the whole population and for two age groups (6-14-and 15-34-year-olds) and used to determine ranges for the relevant criteria.…”

“…Previous work using POM with deterministic models could not distinguish between the two hypotheses for the slow development of immunity against S. haematobium but greatly narrowed down the range of model structures consistent with these field patterns (16). The combination of the life cycle stage that provided the main antigenic stimulus for each antibody response, and the life cycle stage targeted by each antibody response, was critical in determining whether all of these patterns could be reproduced (16). These previous models did not take into account heterogeneities in exposure to infection or look at the distribution of infection or antibody responses across populations nor the impact of treatment on the immune response.…”

“…Here, we extend our earlier POM analysis (16), incorporating heterogeneous exposure into the successful model structures using stochastic individual-based models, and testing whether these models can also reproduce the distributions of S. haematobium infection and antibody seen in the field and the post-treatment antibody switch. We find that only a very limited set of models are capable of reproducing the field data, providing novel insights into the immunological processes that lead to these observed patterns.…”

“…Patternoriented modeling (POM), developed initially in ecology, uses multiple different patterns to discriminate between possible models (15). Previous work using POM with deterministic models could not distinguish between the two hypotheses for the slow development of immunity against S. haematobium but greatly narrowed down the range of model structures consistent with these field patterns (16). The combination of the life cycle stage that provided the main antigenic stimulus for each antibody response, and the life cycle stage targeted by each antibody response, was critical in determining whether all of these patterns could be reproduced (16).…”