PIRCHE-II Is Related to Graft Failure after Kidney Transplantation

Individual HLA mismatches may differentially impact graft survival after kidney transplantation. Therefore, there is a need for a reliable tool to define permissible HLA mismatches in kidney transplantation. We previously demonstrated that donor-derived Predicted Indirectly ReCognizable HLA Epitopes presented by recipient HLA class II (PIRCHE-II) play a role in de novo donor-specific HLA antibodies formation after kidney transplantation. In the present Dutch multi-center study, we evaluated the possible association between PIRCHE-II and kidney graft failure in 2,918 donor–recipient couples that were transplanted between 1995 and 2005. For these donors–recipients couples, PIRCHE-II numbers were related to graft survival in univariate and multivariable analyses. Adjusted for confounders, the natural logarithm of PIRCHE-II was associated with a higher risk for graft failure [hazard ratio (HR): 1.13, 95% CI: 1.04–1.23, p = 0.003]. When analyzing a subgroup of patients who had their first transplantation, the HR of graft failure for ln(PIRCHE-II) was higher compared with the overall cohort (HR: 1.22, 95% CI: 1.10–1.34, p < 0.001). PIRCHE-II demonstrated both early and late effects on graft failure in this subgroup. These data suggest that the PIRCHE-II may impact graft survival after kidney transplantation. Inclusion of PIRCHE-II in donor-selection criteria may eventually lead to an improved kidney graft survival

T-Cell Epitopes Shared Between Immunizing HLA and Donor HLA Associate With Graft Failure After Kidney Transplantation

CD4(+) T-helper cells play an important role in alloimmune reactions following transplantation by stimulating humoral as well as cellular responses, which might lead to failure of the allograft. CD4(+) memory T-helper cells from a previous immunizing event can potentially be reactivated by exposure to HLA mismatches that share T-cell epitopes with the initial immunizing HLA. Consequently, reactivity of CD4(+) memory T-helper cells toward T-cell epitopes that are shared between immunizing HLA and donor HLA could increase the risk of alloimmunity following transplantation, thus affecting transplant outcome. In this study, the amount of T-cell epitopes shared between immunizing and donor HLA was used as a surrogate marker to evaluate the effect of donor-reactive CD4(+) memory T-helper cells on the 10-year risk of death-censored kidney graft failure in 190 donor/recipient combinations using the PIRCHE-II algorithm. The T-cell epitopes of the initial theoretical immunizing HLA and the donor HLA were estimated and the number of shared PIRCHE-II epitopes was calculated. We show that the natural logarithm-transformed PIRCHE-II overlap score, or Shared T-cell EPitopes (STEP) score, significantly associates with the 10-year risk of death-censored kidney graft failure, suggesting that the presence of pre-transplant donor-reactive CD4(+) memory T-helper cells might be a strong indicator for the risk of graft failure following kidney transplantation

Eigenvalue asymptotics for weighted Laplace equations on rough Riemannian manifolds with boundary

Our topological setting is a smooth compact manifold of dimension two or higher with smooth boundary. Although this underlying topological structure is smooth, the Riemannian metric tensor is only assumed to be bounded and measurable. This is known as a rough Riemannian manifold. For a large class of boundary conditions we demonstrate a Weyl law for the asymptotics of the eigenvalues of the Laplacian associated to a rough metric. Moreover, we obtain eigenvalue asymptotics for weighted Laplace equations associated to a rough metric. Of particular novelty is that the weight function is not assumed to be of fixed sign, and thus the eigenvalues may be both positive and negative. Key ingredients in the proofs were demonstrated by Birman and Solomjak nearly fifty years ago in their seminal work on eigenvalue asymptotics. In addition to determining the eigenvalue asymptotics in the rough Riemannian manifold setting for weighted Laplace equations, we also wish to promote their achievements which may have further applications to modern problems

Development and Validation of a Multiplex Non-HLA Antibody Assay for the Screening of Kidney Transplant Recipients

Contains fulltext : 199461.pdf (publisher's version ) (Open Access

Effect of initial immunosuppression on long-term kidney transplant outcome in immunological low-risk patients

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Patient characteristics.

<p>IS: immunosuppression.</p><p>Patient characteristics.</p

Subset distribution of circulating T cells in renal transplant recipients after treatment with tacrolimus, MMF and steroids over time.

<p>(A) Representative dot plots for a renal transplant recipient showing CD3⁺CD4⁺ and CD3⁺CD8⁺ T cells within the CD45⁺ lymphocyte population. Circulating CD4⁺ and CD8⁺ T cells can be characterized as naive (T_N; CD27⁺CD45RO⁻), central memory (T_CM; CD27⁺CD45RO⁺), effector memory (T_EM; CD27⁻CD45RO⁺) and highly differentiated memory (T_EMRA; CD27⁺CD45RO⁻) cells. Furthermore, CD4⁺ T cells can be characterized as regulatory T cells (T_REGS; CD25^hiFOXP3⁺). (B) Shown are the absolute numbers of CD4⁺ T cells and the percentages of T_N, T_CM and T_EM within the CD4⁺ T-cell population for 14 triple immunosuppression-treated patients before transplantation (t = 0) and at 3, 6, 12 and 24 months after transplantation (n = 10 at 24 m). (C) As described under B, for CD8⁺ T cells. (D) The ratio between the percentage of CD4⁺ T_CM or T_EM and the percentage of T_REGS. (E) Longitudinal analysis of the percentages of CXCR3⁺, CCR4⁺, and CCR6⁺ cells within the CD4⁺ T-cell population of 14 triple immunosuppression-treated patients (n = 10 at 24 m). Results are shown as box plots displaying the median, 25^th and 75^th percentiles as the box, and the 5^th and 95^th percentiles as whiskers. Significant differences are indicated compared to pre-transplant levels: *<i>P</i><0.05, **<i>P</i><0.01.</p

Longitudinal analysis of circulating B cells in renal transplant recipients after treatment with tacrolimus, MMF and steroids.

<p>(A) Shown are the absolute numbers of CD19⁺ B cells of 14 triple immunosuppression-treated patients before transplantation (t = 0) and at 3, 6, 12, and 24 months after transplantation (n = 10 at 24 m). (B) Representative dot plots for a renal transplant recipient over time using the Bm1-Bm5 classification: Bm1 (IgD⁺CD38⁻), Bm2 (IgD⁺CD38⁺), Bm2’ (IgD⁺CD38⁺⁺), Bm3+4 (IgD⁻CD38⁺⁺), Early Bm5 (IgD⁻CD38⁺) and Late Bm5 (IgD⁻CD38⁻) cells within the CD19⁺ B-cell population. (C) Shown are the percentages of the different B-cell subsets using the Bm1-Bm5 classification over time. (D) Shown are the percentages of CD80⁺, CD95⁺, and BAFF-receptor⁺ (BAFF-R) cells within the CD19⁺ B-cell population. (E) Overlay plot of the BAFF-R expression (MFI: median fluorescence intensity) within the CD19⁺ B-cell population of one patient before transplantation (pre-Tx) and 24 months (24 m) after transplantation under treatment with tacrolimus, MMF and steroids. Gray line shows unstained cells. (F) Summary graph showing the BAFF-R MFI of 14 triple immunosuppression-treated patients before over time (n = 10 at 24 m). Results are shown as box plots displaying the median, 25^th and 75^th percentiles as the box, and the 5^th and 95^th percentiles as whiskers. Significant differences are indicated compared to pre-transplant (pre-Tx; t = 0) levels: *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001.</p

Longitudinal analysis of circulating B cells in renal transplant recipients after treatment with tacrolimus, MMF and steroids, and a single dose of rituximab (RTX) during transplant surgery.

<p>(A) Representative dot plots of CD19⁺ B cells within the CD45⁺ lymphocyte population for a RTX-treated and a triple immunosuppression (IS)-treated patient before transplantation (pre-Tx) and at 3, 12, and 24 months after transplantation. (B) Shown are the absolute numbers of CD19⁺ B cells for RTX-(gray, n = 12) and triple IS-treated (white, n = 14) patients before transplantation (t = 0) and up to 24 months after transplantation (n = 10 and n = 9 at t = 24 m, respectively). (C) Pie charts depicting the distribution the different B cells subsets over time using the Bm1-Bm5 classification as depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112658#pone-0112658-g003" target="_blank">Figure 3B:</a> Bm1 (IgD⁺CD38⁻), Bm2 (IgD⁺CD38⁺), Bm2’ (IgD⁺CD38⁺⁺), Bm3+4 (IgD⁻CD38⁺⁺), Early Bm5 (IgD⁻CD38⁺) and Late Bm5 (IgD⁻CD38⁻) cells within the CD19⁺ B-cell population. Data are represented as means of 14 triple IS+RTX-treated and 12 IS-treated patients before transplantation (pre-Tx) and at 3, 12, and 24 months after transplantation (n = 10 and n = 9 at t = 24 m, respectively). (D) Shown are the percentages of CD80⁺, CD95⁺ and BAFF-R⁺ cells within the CD19⁺ B-cell population for RTX- (gray, n = 12) and triple IS-treated (white, n = 14) patients before transplantation (t = 0) and up to 24 months after transplantation. Results are shown as box plots displaying the median, 25^th and 75^th percentiles as the box, and the 5^th and 95^th percentiles as whiskers. Significant differences are indicated by asterisks: **<i>P</i><0.01.</p

<i>Ex vivo</i> cytokine production by circulating T cells in renal transplant recipients after treatment with tacrolimus, MMF and steroids.

<p>Peripheral blood mononuclear cells (PBMCs) were stimulated for 4 hours in the presence of PMA, ionomycin and Brefeldin A. Shown are the percentages IL-2, IL-4, IL-17, IFNγ or TNFα-producing cells within the CD4⁺ or CD8⁺ T-cell population of 14 triple immunosuppression-treated patients before transplantation (t = 0) and at 3, 6, 12, and 24 months after transplantation (n = 10 at 24 m). Results are shown as box plots displaying the median, 25^th and 75^th percentiles as the box, and the 5^th and 95^th percentiles as whiskers. Significant differences are indicated compared to pre-transplant levels: *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001.</p