Why does Mycobacterium tuberculosis use multiple mechanisms to inhibit antigen presentation

1. Why does Mycobacterium tuberculosis use multiple mechanisms to inhibit antigen presentation? Stewart Chang Bioinformatics Program @ The University of Michigan Advisors: Denise Kirschner and Jennifer Linderman

2. The macrophage during M. tuberculosis infection Dual roles during TB infection Preferred host cell Effector cell Effector cell function is cell-mediated Requires cytokine signal from CD4+ T helper cell Macrophages must first present antigen bound to MHC class II

3. One M. tuberculosis survival strategy:Inhibit antigen presentation Experimental method: Infect monocytes with M. tuberculosis at MOI 50 Add soluble model antigen (tetanus toxoid) Measure T cell response by thymidine uptake Infected monocytes do not stimulate T cells as well as uninfected monocytes

4. A number of different mechanismshave been hypothesized Inhibition of antigen processing (H1) Inhibition of MHC class II maturation (H2) Inhibition of MHC class II peptide loading (H3) Inhibition of MHC class II transcription (H4)

5. Conflicting data in the literature

6. Questions asked in this study Why have multiple mechanisms been observed? What purpose do multiple mechanisms serve? Do some experimental protocols favor the detection of particular mechanisms? Do alternative mechanisms exist?

7. A review of antigen presentation Two pathways: one for endogenous antigens, the other for exogenous antigens MHC class II acts as a receptor for peptides derived from exogenous antigens

8. The antigen presentation model Above a certain threshold, the number of surface MHC class II-peptide complexes is determinative of T cell response Therefore, we use surface MHC class II-peptide complexes as our output variable

9. Model testing Parameter values were derived from the literature (mouse) Model behavior was checked against experimental results At right, behavior when IFN-g was added

10. Additional model testing: Antigen presentation

11. Simulations of hypothesized mechanisms, effects on antigen presentation levels In simulations, IFN-g and antigen were added at time 0 h, and relevant processes were inhibited to same extent Effect of H1 or H3 immediate but attenuates at 1 h and 10 h H2 or H4 effective at time points > 10 h Mechanisms may be complementary and allow M. tuberculosis to continuously inhibit antigen presentation

12. Application of the model to previous experimental protocols Goal: To determine if some experimental protocols favored the detection of particular M. tuberculosis mechanisms Two previous protocols: Model accounts for differences in timings and concentrations but not differences in macrophage cell lines or M. tuberculosis strains

13. Surprising results for protocol of Noss et al. (2000) In agreement with Noss et al. (2000), inhibiting MHC class II transcription (H4) significantly decreased antigen presentation levels However, inhibiting antigen processing (H1) or MHC class II peptide loading (H3) had a negligible effect on antigen presentation levels

14. Overview of sensitivity analysis Allows you to determine importance of inputs (e.g. parameters) to output variable Rationale: Incomplete knowledge of parameters and extent to which M. tuberculosis inhibits processes Methodology, in general: Specify distribution for each input, sample using LHS. For each set of input values, generate an output value (above right). Calculate correlation coefficient (e.g. PRCC) between output and input values. Plot correlation coefficients versus time to identify important inputs (below right). We specify uniform distributions with boundaries 10% and 190% of baseline values

15. Sensitivity analysis reveals other possible mechanisms

16. Return to the question:Why multiple mechanisms? May allow continuous inhibition of antigen presentation Otherwise, inhibition may either abate with time or be delayed Our simulations of previous experimental protocols produce results consistent with their respective studies However, these protocols may favor detection of mechanisms targeting MHC class II expression Other mechanisms may be possible Possible targets: IFN-g receptor-ligand binding, lysosomal degradation of antigen

17. Another application of the model:Aid design of new experimental protocols

18. Predicted results using the proposed protocol Let Q = percent reduction in antigen presentation levels of infected macrophages compared to uninfected control Q stays constant to the extent that mechanisms targeting processes other than MHC class II expression are effective

19. Current directions: Applying the ODE model to M. tuberculosis antigens and MHC class II alleles Important M. tuberculosis antigens are known: Antigen 85 complex: Ag85A, Ag85B, Ag85C 6-kDa early secretory antigenic target (ESAT-6) But many parameters need to be determined, e.g. binding affinity to MHC Some MHC class II alleles increase susceptibility to TB: e.g. HLA-DR2 (old nomenclature), HLA-DRB1*1501 Some MHC class II alleles decrease susceptibility to TB: e.g. HLA-DR3 Generally believed that MHC class II alleles differ in ability to bind peptides, but what happens at the macrophage surface? Hypothesis: MHC class II from different alleles Differ in ability to bind Mtb antigens Leads to different numbers of MHC-Mtb antigen complexes on macrophage surface Elicits different T helper cell responses

20. In lieu of experimental data for M. tuberculosis antigens, statistical models to predict binding affinity A published additive model to predict binding affinity: Step 1: Measure IC50 of standard peptide Step 2: Measure IC50 of derivatives (differ by only 1 aa = perfect data set) Step 3: Find ratios of derivatives’ IC50 to standard’s IC50 Step 4: Multiply ratios for peptide of interest Step 5: Multiply by IC50 of parent peptide (here, A13) Authors’ claim: Predicts IC50 to within one order magnitude (peptides may vary five)

21. Do DR2 and DR3 differ in their binding affinities for Ag85B? Target set: Mtb Ag85B epitopes (18mers) recognized by CD4+ T cells Trained model on 18mers in JenPep database (www.jenner.ac.uk/jenpep) Predicted IC50 values (binding affinities) differ by as much as two-fold

22. Another view of the preliminary data The difference between binding affinities is statistically significant Could this account for differences in immune response? Or, could this result in different numbers of MHC-Ag85B complexes on the macrophage surface and different T cell responses? These numbers could be used in the ODE model to generate experimentally testable predictions

23. Acknowledgments Kirschner lab members Linderman lab members Helpful discussions: Cheong-Hee Chang, Joanne Flynn, Eugenio de Hostos