Conclusions

1. Computer simulation of the immune system: study of cross-reactive Tc memory against heterologous virusesYiming Cheng, Dario Ghersi, Claudia Calcagno, Roberto Puzone, Franco CeladaDivision of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA Model Setup Abstract IMMSIM[1] was constructed to tackle the staggering complexity of the Immune System by comparing it with the responses of an agent-based computer model, where the agents are minimalistic portraits of lymphoid cell types, body’s target cells and typical invaders. Once launched, the response develops in virtual 3D (2 dimensions in space and 1 dimension in time) computer spaces, and all similarities with and differences from biological consensus serve to shape, confirm or discard ideas and hypotheses. In the present study we simulate the formation and the recall of anti-virus memory, and try to predict what will happen both to the responses and to the memory cells when the second infecting virus is partly different from the first one, and when the cross-reactivity of the two branches, humoral and cellular of the immune system is surprisingly asymmetrical, or split. The in machina simulations explore all epitope distances, and measure all changes in affinity, cellularity and efficiency in clearing the infection. Besides the obvious cooperation, they reveal powerful competitions between the two branches and between the cross-reacting memory and new responses when the latter suffer the competition by preformed cell-rich but inefficient clones. Memory, usually an asset, has become a liability. Cross-reactivity Split cross-reactivity The experiments have been conducted in a MATRIX scheme to ensure at least 25 repetitions of each experiment for every one bit at a time increased distance between the primary infectant and the challenging virus. Since only 0, 1, 2 and 3 mismatches between paratope and epitope are effective, 2 viruses are potentially related when they carry up to 6 bit differences between their respective peptides, as shown in Cartoon (A). Each virtual mouse (VM) in the present setup is endowed with 2500 cells of each type: B, Th (Th1+Th2), Tc, APC. The random generation of 16-bit strings assures a total repertoire of 65536 different specificities for B and T receptors. Each of the specific cell types (B and T) is endowed with the clonal repertoires shown in the table. Selin et al. [2] discovered that many serologically heterologous viruses are cross-reactive at the level of cellular responses. This is defined as Split cross-reactivity[3]. (A) (B) maps all distances between 5 viruses; (C) shows the combinations between primary and secondary challenge used in the matrix. Running side-by-side H+C and C (Split) responses allowed us to dissect two zones of contrast affecting the cellular response. (a) The Tc strength recall at bit distance 0-3 suffers a competitive thwarting caused by the presence of the humoral response. The probable effector is antibody, as indicated by what-if experiments introducing virtual Ab half lifetime. The overlap of specificity in two randomly generated VM is illustrated in the figure below and is compatible with the consensus on private specificity. IMMSIM model (B) (C) The agents of IMMSIM behave like cellular automata (a), but they represent all types of cells of the immune system with their specific receptors and paratopes, the antigens and the invading viruses. T cell helps B cell upon antigen binding of the B receptor, leading to the initiation of the humoral response (b). The maturation of the antibody response is governed by the scarcity of T help in the primary response and the antigen competition in the secondary response (c). (d) illustrates all simulated steps of the viral infection and the combined cellular and humoral response leading to the cure of infection. (a) Homologous Responses Repeated infections by the same viruse generate primary, secondary and tertiary responses by the humoral and cellular branches of the immune system. At each successive infection, the number of virus particles decreases Recall efficiency, in terms of clones and of cell numbers, declines in parallel in the humoral response, but the decline is biphasic in the cellular, with no decrease up to bit distance 3. (b) (a) (b) The Tc strength at bit distance 2-4 is thwarted and this is caused by the presence of cellular cross-reactive memory. H C The strength (weighted affinity) of the secondary response relative to the primary response toward the primary infectant and against the secondary infectant. In the humoral response, the latter curve descends harmoniously from 1.3 to 1 as expected. In the cellular response, there is an initial dip and the level of the primary response is reached only at distance 5 and higher. (c) (b) V Responses are decreasing in clone repertoire. H C Conclusions (d) How can memory be a burden instead of an asset? It can, if the affinity of memory is low on account of the distance of the cross-reactants: the sheer member of the memory cells may outcompete the naïve cells and offer a lower quality defense. Like all model results, these conclusions are provisional. They await the conformation by wet-lab researches (of the UMASS team) before being used in the discussion about – e.g. – the influenza vaccine. This work is supported by NIH Grant R01-AI054455 H C Comparing the clonal engagement in the secondary response relative to the primary for humoral and cellular responses show trimming at bit distance 0, 1, 2, 3, 4 and then expansion at distance 5-8. The significance of this observation is under study. Responses are increasing in clone average affinity (strength). References 1. Celada F, Seiden PE. A computer model of cellular interactions in the immune system. Immunol Today 1992;13(2):56-62 2. Selin LK, Lin MY, Kraemer KA, Pardoll DM, Schneck JP, Varga SM, et al. Attrition of T cell memory: selective loss of LCMV epitope-specific memory CD8 T cells following infections with heterologous viruses. Immunity 1999;11(6):733-42 3. Celada F, 2005, Personal Communication. H C C H