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. 2010 Oct 14;5(10):e13358.
doi: 10.1371/journal.pone.0013358.

Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory T, monocytes and B cells

Yevgeniy A Grigoryev  1 , Sunil M Kurian, Zafi Avnur, Dominic Borie, Jun Deng, Daniel Campbell, Joanna Sung, Tania Nikolcheva, Anthony Quinn, Howard Schulman, Stanford L Peng, Randolph Schaffer, Jonathan Fisher, Tony Mondala, Steven Head, Stuart M Flechner, Aaron B Kantor, Christopher Marsh, Daniel R Salomon
Affiliations

Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory T, monocytes and B cells

Yevgeniy A Grigoryev et al. PLoS One. .

Abstract

A major challenge for the field of transplantation is the lack of understanding of genomic and molecular drivers of early post-transplant immunity. The early immune response creates a complex milieu that determines the course of ensuing immune events and the ultimate outcome of the transplant. The objective of the current study was to mechanistically deconvolute the early immune response by purifying and profiling the constituent cell subsets of the peripheral blood. We employed genome-wide profiling of whole blood and purified CD4, CD8, B cells and monocytes in tandem with high-throughput laser-scanning cytometry in 10 kidney transplants sampled serially pre-transplant, 1, 2, 4, 8 and 12 weeks. Cytometry confirmed early cell subset depletion by antibody induction and immunosuppression. Multiple markers revealed the activation and proliferative expansion of CD45RO(+)CD62L(-) effector memory CD4/CD8 T cells as well as progressive activation of monocytes and B cells. Next, we mechanistically deconvoluted early post-transplant immunity by serial monitoring of whole blood using DNA microarrays. Parallel analysis of cell subset-specific gene expression revealed a unique spectrum of time-dependent changes and functional pathways. Gene expression profiling results were validated with 157 different probesets matching all 65 antigens detected by cytometry. Thus, serial blood cell monitoring reflects the profound changes in blood cell composition and immune activation early post-transplant. Each cell subset reveals distinct pathways and functional programs. These changes illuminate a complex, early phase of immunity and inflammation that includes activation and proliferative expansion of the memory effector and regulatory cells that may determine the phenotype and outcome of the kidney transplant.

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Conflict of interest statement

Competing Interests: There are two competing interests involved in this study. 1) Roche Pharmaceuticals employed the following authors: Z. Avnur, D. Borie, T. Nikolcheva, A. Quinn, and S.L. Peng. Roche funded part of this work as an unrestricted research grant to Dr. Daniel R. Salomon, the senior author. The rest of the work was supported by NIH grant AI063603 to Dr. Salomon. These employees of Roche were scientific collaborators and involved in the development of the study design and reviewed the first part of the data generated. Roche has relinquished any patent interests or IP ownership in anything involved in this project. Dr. Salomon and his collaborating scientists at Scripps and The Cleveland Clinic have no ongoing consultant or speaker arrangements of any kind with Roche and no stock, stock options or other forms of equity in Roche. 2) PPD employed the following authors during the time this work was done: J. Deng, A.B. Kantor, and H. Schulman. PPD provided the Surroscan high throughput flow cytometry work in the manuscript and their participation was funded by Roche as a combination of fee for service, and then consultant fees for the data evaluation and presentation phase of the Roche-funded portion of the work. PPD has never had any patent, IP or other rights in this work. These competing interests do not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials as outlined at the journal website online.

Figures

Figure 1
Figure 1. Serial cytometry profiling of cell subset populations shows distinct patterns of depletion and recovery early post transplantation.
(A). Absolute cell counts were determined and expressed as a percent of the counts pre-treatment. Multiple changes were observed in peripheral blood cell populations following transplantation, induction antibody therapy and drug-based immunosuppression. The different major cell populations show distinct patterns of depletion, gain and recovery as a function of time in the first 12 weeks, which we have defined as the “early” post transplant period. The gradual recovery of each of the subsets in time after transplant demonstrate that a proliferative expansion of at least subpopulations of these cells is underway despite the many individual differences observed. (B). T cell counts and mRNA show the same pattern of depletion and recovery. Cytometry variables for comparison to gene expression included: cell counts and expression intensity – mean per cell. CD3 intensity per cell is constant with treatment. The product of count and intensity (Count x Int) integrates the expression of each marker on each subpopulation at each time point and is the sum of the mean intensities of the given marker multiplied by all the cells identified in that population at any given time point. Count x Int. gives the same relative to baseline as absolute counts, indicating that the loss of T cells, not CD3 intensity, is the key changing parameter. Gene expression shows a consistent pattern for the 8 variables representing known T cell markers and correlates well with T cell counts. This is the first example of a proteogenomic validation of the results.
Figure 2
Figure 2. HLA Class II intensity levels for B cells and monocytes increase in time.
HLA Class II expression intensity as percent of baseline is shown on the Y-axis, while weeks post transplant/induction is shown on the X-axis. Plotted are intensities for antibodies staining HLA-DP, DQ, DR and a pan HLA-reactive antibody for B cells and monocytes. These results clearly show the progressive upregulation of HLA antigen expression on both cell types consistent with a progressive immune activation that is supported by concomitant expression of multiple activation markers as described in the Results.
Figure 3
Figure 3. Gene expression profiling and functional analysis of whole peripheral blood.
(A). A Pie chart summary for the number of differentially expressed genes in whole blood obtained in each time class-comparison analysis (p<0.001). The size of each slice represents the percentage of genes in that class based on the total of differentially expressed genes identified in all of the analyses done. Significant differential gene expression for each Post-TX timepoint was determined against the Pre-TX samples and these are represented in this figure as “Pre-TX vs. Week 1” and so on. In parallel, we performed ANOVA comparisons for all timepoints to determine what we have termed the “multivariate” genes that change significantly and differentially in a coordinate fashion at all timepoints post transplantation. (B). Functional analyses of the significant differentially expressed genes populating statistically significant Ingenuity pathways. The results for 5 timepoint comparisons: Pre-TX vs. week 1, 2, 4, 8 and 12 are shown in different colors. The Y-axis depicts the % of genes identified in our results vs. the total number of genes known to populate the pathway in the literature upon which Ingenuity mapping is based. We identified 4 different classes of differentially expressed genes mapping to these functional pathways: early, late, consistent and intermittent. The majority of genes are in the early and consistent classes. (C). Pie chart representing the 19 significant functional pathways populated by 134 significantly differentially expressed multivariate genes, with percentage of multivariate genes per total genes in a pathway. On the right, the 19 functional pathways shown in the pie chart are ranked by their p-value significance and includes the % genes populated per pathway, and the total number of genes identified for each pathway in our samples.
Figure 4
Figure 4. Gene expression profiling and functional analysis of the CD4 cell subset.
(A). Schema representing our analysis of differential gene expression and the mapping of functional pathways for the CD4 T cell subset. This work was based on two time comparisons to clarify the evolution of changes during the first 12 weeks post transplantation: Pre-TX vs. Week 2 and Pre-TX vs. Week 12. (B). Functional pathways populated by genes differentially expressed early (blue), late (red) or shared at both timepoints Post-TX up to Week 12 in the CD4 T cell subset. The Y-axis represents % genes populated per pathway in our data based on the total number of genes identified by Ingenuity for each pathway.
Figure 5
Figure 5. Gene expression profiling and functional analysis of the CD8 cell subset.
Top 22 populated functional pathways for genes differentially expressed early (blue), late (red) or at both timepoints Post-TX in the CD8 T cell subset. Y-axis represents % genes present per pathway. For the complete list of functional pathways, see Table S4.
Figure 6
Figure 6. Gene expression profiling and functional analysis of the CD14 monocyte and CD19 B cell subsets.
(A). Functional pathways populated by genes differentially expressed early (blue), late (red) or shared at both timepoints Post-TX in the CD14 monocyte subset. Y-axis represents % genes present per pathway. (B). Functional pathways populated by genes differentially expressed early (blue), late (red) or shared at both timepoints Post-TX in the CD19 B cell subset. Y-axis represents % genes present per pathway.
See this image and copyright information in PMC

References

    1. Barton GM. A calculated response: control of inflammation by the innate immune system. J Clin Invest. 2008;118:413–420. - PMC - PubMed
    1. Perco P, Pleban C, Kainz A, Lukas A, Mayer B, et al. Gene expression and biomarkers in renal transplant ischemia reperfusion injury. Transpl Int. 2007;20:2–11. - PubMed
    1. Kamoun M. Mechanisms of chronic allograft dysfunction. Ther Drug Monit. 2006;28:14–18. - PubMed
    1. Boros P, Bromberg JS. New cellular and molecular immune pathways in ischemia/reperfusion injury. Am J Transplant. 2006;6:652–658. - PubMed
    1. Gould SJ, Hildreth JE, Booth AM. The evolution of alloimmunity and the genesis of adaptive immunity. Q Rev Biol. 2004;79:359–382. - PubMed

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