Proteolytic dynamics of human 20S thymoproteasome

Abstract

An efficient immunosurveillance of CD8⁺ T cells in the periphery depends on positive/negative selection of thymocytes and thus on the dynamics of antigen degradation and epitope production by thymoproteasome and immunoproteasome in the thymus. Although studies in mouse systems have shown how thymoproteasome activity differs from that of immunoproteasome and strongly impacts the T cell repertoire, the proteolytic dynamics and the regulation of human thymoproteasome are unknown. By combining biochemical and computational modeling approaches, we show here that human 20S thymoproteasome and immunoproteasome differ not only in the proteolytic activity of the catalytic sites but also in the peptide transport. These differences impinge upon the quantity of peptide products rather than where the substrates are cleaved. The comparison of the two human 20S proteasome isoforms depicts different processing of antigens that are associated to tumors and autoimmune diseases.

Keywords: antigen processing; bioinformatics; computational biology; proteasome; protein degradation; proteolysis; thymoproteasome; thymus.

Figure 1.

Overview of the computational modeling approach describing the proteasome proteolytic dynamics. In A, the schematic of the compartmentalized proteasome model proposed by Liepe et al. (11) is shown. The model describes all relevant steps involved in substrate degradation. These include (i) peptide transport steps (peptide binding close to the outer site of the gate, peptide influx into the chamber, peptide translocation inside the chamber, and peptide efflux out of the chamber), (ii) substrate hydrolysis steps (peptide binding to the active site and subsequent hydrolysis and peptide binding to the noncatalytic inhibitor site), and (iii) transport regulation (peptide binding to the noncatalytic enhancer site, peptide binding to the noncatalytic inhibitor site, and resulting effects on the conformation of the proteasome gate). The gray chamber represents a simplification of the 20S proteasome catalytic chamber with openings to the outside. The substrate and product peptides (purple) can enter the 20S proteasome chamber upon binding to the outer face of the gate, interact with the regulatory and catalytic sites inside the chamber, and leave the proteasome chamber upon translocation to the proximity of the inner face of the gate. Gray arrows indicate the transport of substrate and product peptides. The orange arrow denotes the hydrolysis reaction where a substrate peptide is transformed into product peptide, thereby releasing the fluorophore. Enhancing regulatory sites inside the chamber are shown in blue with the dashed arrows indicating their effect (transport-enhancing gate conformation). The inhibiting regulatory site outside the chamber is shown in light blue with the dashed arrow indicating its effect (transport-inhibiting gate conformation). The catalytic site consists of an active site (light orange) and an inactive modifier site (dark orange). For details of the model equations, model setup, and model parameters, please refer to Liepe et al. (11). In B, the schematic of Bayesian inference is sketched. Computational models describing biological systems are often parameterized. These parameters can be abstract, or as is the case here, they can be kinetic parameters with a direct physical translation. To learn anything from the model, it is necessary to calibrate the model against experimental data. This can be done in many fashions; however, in the last decade Bayesian inference techniques proved to be powerful model calibration tools. One of the advantages of Bayesian inference is that it estimates not only the model parameters but also their uncertainty. In general, experimental data are collected and a computational model is formulated. Both are then used as input for the Bayesian inference algorithm (here approximate Bayesian computation). The basic concept of the algorithm is to test all sorts of combinations of parameters (through a defined sampling scheme) and simulate the model with those parameter combinations. If the model simulations correspond well to the experimental data, the corresponding parameter combination is accepted; otherwise the parameter combination is discarded. This is done repeatedly until a certain number of accepted parameter combinations is reached, which then construct the so-called posterior parameter distribution. This posterior distribution contains all information about the separate model parameters as well as their dependences among each other. The outputs of Bayesian inference are therefore the model fits of the experimental data and the posterior parameter distributions. Calibrating the same model to experimental data generated under different conditions (here different proteasome isoforms) allows us to compare the obtained posterior parameter distributions and detect which model parameters differ for the different conditions and which parameters are not influenced.

Figure 2.

Catalytic β subunit expression in different human cell lines. A, RT-PCR products specific for PSMB11 (β5t subunit) or actin (as control) obtained from different immortalized or tumor-derived human cell lines. The PSMB11-specific band is marked with a red arrow. Specificity has been confirmed by cloning and sequencing the cDNA extracted from the band. B, MS identification of the β5t subunit in the cell lysate of the same cell lines shown in A. Cell lysates have been separated on an SDS gel and stained with Coomassie (left panel; only some representative cell samples are shown), the proteins framed in the picture have been cut and digested by trypsin, and the β5t subunit–specific peptides have been detected by MS. Only for the C5.5 cell line have we identified the β5t subunit products (marked with bold letters; 19 specific peptides depicted below the gel) with significant MS/MS spectra (the spectrum of one of them is depicted in the right panel). C, Western blot assays for proteasome catalytic subunits, which have been carried out after separation of 0.5 μg of purified 20S proteasomes by SDS-PAGE, are shown. D, relative quantification of the subunits β5 (PSB5_human) and β5t (PSB11_human) of purified 20S proteasome after absolute quantification with AQUA peptides. Shown are representative MALDI mass spectra of the tryptic peptide ²²⁶DAYSGGAVNLYHVR²³⁹ with [M + H]⁺_exp = 1521.78 and its heavy analogue with [M + H]⁺_exp = 1528.80 of β5 subunit at spot 42 and ²¹⁶DAYSGGSVDLFHVR²²⁹ with [M + H]⁺_exp = 1522.77 and its heavy analogue with [M + H]⁺_exp = 1529.79 of β5t subunit at spot 69. The calculation of the absolute amount of the tryptic peptides is performed by comparison of the MS peak areas with those of the corresponding AQUA peptides.

Figure 3.

Computational model parameters and rate-limiting steps of proteasome isoform dynamics. A and B, marginal posterior parameter distributions obtained by calibrating the proteasome kinetic model against experimental data (n = 3–5) derived from the degradation of the substrate Suc-LLVY-MCA by 20S s-, i-, and t-proteasomes. Parameters are grouped into active site–related parameters (A) and transport- and transport regulation–related parameters (B). Briefly, K_aS and K_aP are the dissociation constant of substrate (S) and product (P) to active site(s); k_p is the peptide-bond hydrolysis rate at active site(s); β is the factor by which k_p is multiplied upon binding to inhibitory site(s); n_a and n_i are the Hill coefficients for binding to the active site(s) and the inhibitor site(s); K_iS and K_iP are the dissociation constants of substrate (S) and product (P) to inhibitor site(s); v_in and v_out are the peptide influx and efflux rates, and v_in/v_out is their ratio; k_off/k_on is the ratio between the dissociation and the association rates to the gate; and R_off/R_on is the ratio between the unbinding and binding rates to the enhancing regulator site(s). The meaning of all parameters is depicted in Fig. 1 and Table 1. C and D, analysis of rate-limiting steps in 20S i- and t-proteasomes. Depicted is the -fold change of product formation (y axis) upon increase (by a factor of; x axis) of a specific reaction step for the degradation of Suc-LLVY-MCA (C) and Z-LLE-MCA (D) as simulated by our computational model of the 20S proteasome dynamics. The initial substrate concentration for this analysis is 160 μm, and the -fold change is determined after 60-min reaction relative to the experimentally measured proteasome kinetics (factor = 1). The mean of 1000 in silico predictions (colored lines) is plotted over time for the degradation of the substrates with the same initial substrate concentrations as in the experiments. The rate-limiting steps are those for which the increase leads to the largest -fold change.

Figure 4.

Simulation of the substrate and product dynamics inside the chambers of the human s-, i-, and t-proteasomes. The mean of 1000 in silico predictions (colored lines) is plotted over time for the degradation of the substrate Z-LLE-MCA (A) or Suc-LLVY-MCA (B) with the same initial substrate concentrations as used in the independent experiments (n = 3–5; see “Material and methods”). The simulation has been performed for 20S i- and t-proteasomes. The number of peptide molecules (product and substrate) and the relative amount of products versus total amount of peptides inside the chambers over time have been computed by the computational model and are based on the estimated posterior parameter distributions for each substrate and proteasome isoform.

Figure 5.

Different proteasome isoforms preferentially use different substrate cleavage sites. A–D, the relative frequency of the substrate cleavage-site usage (i.e. the SCS of the synthetic substrates gp100^201–230 (A), MBP^102–129 (B), MOG^172–202 (C), and gp100^35–53 (D) by 20S s-, i-, and t-proteasome are shown as the mean of two to four independent experiments (error bars are the S.D.) measured two to three times. Quantitative analyses are done by applying QME to the MS measurements.

Figure 6.

Proteasome isoforms overall share a common substrate cleavage-site usage with specific differences. The correlation between the SCSs of the synthetic substrates gp100^201–230 (pink circles), gp100^35–53 (blue circles), MBP^102–129 (green circles), and MOG^172–202 (black circles) generated in in vitro digestion kinetics by human 20S s-, i-, or t-proteasomes is shown. The SCSs are compared in proteasomes pairwise. The computed Pearson's correlation coefficients and the tests for significance of these correlations are reported. Nonsignificant correlations are marked in bold and depict SCSs that differ between the pair of compared 20S proteasome types. The substrate cleavage-sites which the use of is more remarkably varied between 20S proteasome isoforms are labeled.

Figure 7.

Proteasome isoforms generate MBP epitopes with different kinetics. The degradation kinetics of the synthetic substrates MBP_102–129 by 20S s-, i-, and t-proteasomes and the generation kinetics of the epitopes MBP_111–119 and MBP_107–115 are shown. The mean of two to four independent experiments (error bars are the S.D.) measured in duplicate is shown. Quantitative analyses are done by applying QME to the MS measurements.