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      Quantitative Analysis of Immune Response and Erythropoiesis during Rodent Malarial Infection

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          Abstract

          Malarial infection is associated with complex immune and erythropoietic responses in the host. A quantitative understanding of these processes is essential to help inform malaria therapy and for the design of effective vaccines. In this study, we use a statistical model-fitting approach to investigate the immune and erythropoietic responses in Plasmodium chabaudi infections of mice. Three mouse phenotypes (wildtype, T-cell-deficient nude mice, and nude mice reconstituted with T-cells taken from wildtype mice) were infected with one of two parasite clones (AS or AJ). Under a Bayesian framework, we use an adaptive population-based Markov chain Monte Carlo method and fit a set of dynamical models to observed data on parasite and red blood cell (RBC) densities. Model fits are compared using Bayes' factors and parameter estimates obtained. We consider three independent immune mechanisms: clearance of parasitised RBCs (pRBC), clearance of unparasitised RBCs (uRBC), and clearance of parasites that burst from RBCs (merozoites). Our results suggest that the immune response of wildtype mice is associated with less destruction of uRBCs, compared to the immune response of nude mice. There is a greater degree of synchronisation between pRBC and uRBC clearance than between either mechanism and merozoite clearance. In all three mouse phenotypes, control of the peak of parasite density is associated with pRBC clearance. In wildtype mice and AS-infected nude mice, control of the peak is also associated with uRBC clearance. Our results suggest that uRBC clearance, rather than RBC infection, is the major determinant of RBC dynamics from approximately day 12 post-innoculation. During the first 2–3 weeks of blood-stage infection, immune-mediated clearance of pRBCs and uRBCs appears to have a much stronger effect than immune-mediated merozoite clearance. Upregulation of erythropoiesis is dependent on mouse phenotype and is greater in wildtype and reconstitited mice. Our study highlights the informative power of statistically rigorous model-fitting techniques in elucidating biological systems.

          Author Summary

          Malaria is a disease caused by a protozoan parasite of the genus Plasmodium. Every year there are around 250 million human cases of malaria, resulting in around a million deaths. Most of the severe cases and deaths are due to Plasmodium falciparum, which is endemic in much of sub-Saharan Africa and other tropical areas. The pathology of malaria is related to the asexual stage of the parasite. Understanding the infection dynamics during this stage is therefore essential to inform malaria treatment and vaccine design. Experimental infections of rodents represent an important first step towards understanding the more complicated human infections. We developed a series of models representing different hypotheses about the main processes regulating the infection dynamics during the asexual stage. Models were fit to data on Plasmodium chabaudi infections of mice, using a Bayesian statistical framework. The accuracy of different models in explaining the RBC and parasite densities was quantified. We identify the role of different types of immune-mediated mechanism, and show that RBC production (erythropoiesis) increases during infection. Differences between mouse phenotypes are explained. Our study highlights the informative power of model-fitting techniques in explaining biological systems.

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          Most cited references18

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          Anaemia of acute malaria infections in non-immune patients primarily results from destruction of uninfected erythrocytes.

          While anaemia has long been recognized as a consequence of acute infections with malaria, the relative contributions of direct erythrocyte destruction by parasites, destruction of uninfected erythrocytes and changes in erythropoiesis have been unclear. Fitting of parasitaemia and anaemia data from neurosyphilis patients undergoing malaria therapy to a mathematical model shows that in these patients, an average of 8.5 erythrocytes were destroyed in addition to each erythrocyte observed to become parasitized. The model also showed that dyserythropoiesis plays an insignificant role in the resulting anaemia. The anaemia occurs before a substantial antibody response to parasites or erythrocytes could be generated. We postulate that uninfected erythrocyte destruction occurs through phagocytosis of erythrocytes bound to merozoites killed as a result of the accompanying malaria paroxysms.
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            Absence of thymus in a mouse mutant.

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              Transient cross-reactive immune responses can orchestrate antigenic variation in malaria.

              The malaria parasite Plasmodium falciparum has evolved to prolong its duration of infection by antigenic variation of a major immune target on the surface of the infected red blood cell. This immune evasion strategy depends on the sequential, rather than simultaneous, appearance of immunologically distinct variants. Although the molecular mechanisms by which a single organism switches between variants are known in part, it remains unclear how an entire population of parasites within the host can synchronize expression to avoid rapidly exhausting the variant repertoire. Here we show that short-lived, partially cross-reactive immune responses to parasite-infected erythrocyte surface antigens can produce a cascade of sequentially dominant antigenic variants, each of which is the most immunologically distinct from its preceding types. This model reconciles several previously unexplained and apparently conflicting epidemiological observations by demonstrating that individuals with stronger cross-reactive immune responses can, paradoxically, be more likely to sustain chronic infections. Antigenic variation has always been seen as an adaptation of the parasite to evade host defence: we show that the coordination necessary for the success of this strategy might be provided by the host.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                September 2010
                September 2010
                30 September 2010
                : 6
                : 9
                : e1000946
                Affiliations
                [1 ]Centre for Infectious Diseases, Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
                [2 ]Department of Animal Ecology, Lund University, Lund, Sweden
                [3 ]Center for Infectious Disease Dynamics and Departments of Biology and Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America
                University of New South Wales, Australia
                Author notes

                Conceived and designed the experiments: LR AFR. Performed the experiments: LR. Analyzed the data: MRM NJS. Contributed reagents/materials/analysis tools: LR. Wrote the paper: MRM LR AFR NJS. Conceived and designed the mathematical models: NJS MRM. Conceived and designed the statistical methods: NJS.

                Article
                10-PLCB-RA-2211R2
                10.1371/journal.pcbi.1000946
                2947982
                20941388
                732541a2-a7ea-4cc4-a3a6-db9cb9f8f8ad
                Miller et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 8 May 2010
                : 31 August 2010
                Page count
                Pages: 15
                Categories
                Research Article
                Computational Biology
                Immunology/Immune Response
                Mathematics/Statistics
                Public Health and Epidemiology/Infectious Diseases

                Quantitative & Systems biology
                Quantitative & Systems biology

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