Research ArticleGraft-Versus-Host Disease

Transcriptome analysis of GVHD reveals aurora kinase A as a targetable pathway for disease prevention

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Science Translational Medicine  25 Nov 2015:
Vol. 7, Issue 315, pp. 315ra191
DOI: 10.1126/scitranslmed.aad3231
  • Fig. 1. Sirolimus and Tac-Mtx provide graded disease protection in an NHP model of GVHD.

    (A) Experimental schema detailing the transplant protocol and immunoprophylaxis regimens used throughout this study. (B) Clinical score from a cohort of animals after allogeneic or autologous (Auto; black) HCT. Allogeneic transplants received the following immunoprophylactic regimens: (i) none (No Rx; red), (ii) sirolimus monotherapy (Siro; blue), and (iii) tacrolimus-methotrexate (Tac-Mtx; green). Scoring was based on our previously described NHP GVHD clinical scoring system (5). Significance of clinical scores for animals with GVHD was determined by unpaired t test. Tac-Mtx score was found to be significantly lower on day 7 than both No Rx (P = 0.0013) and Siro scores (P = 0.033). (C) Comparison of survival curves between all groups undergoing HCT. The Kaplan-Meier product-limit method was used to calculate survival. Significance between the immunoprophylaxis and No Rx groups was determined using log-rank statistics with *P < 0.05. A near significant difference in survival was observed between sirolimus and No Rx cohort (P = 0.055). (D) Donor chimerism as determined by donor-specific microsatellite-based analysis in the allogeneic HCT cohorts (5). (E) Engraftment of white blood cells (WBCs; by complete blood count), T cells (by flow cytometric analysis of CD3+/CD20 cells), CD4+ T cells (by flow cytometric analysis of CD3+/CD20/CD4+/CD8 cells), and CD8+ T cells (by flow cytometric analysis of CD3+/CD20/CD4/CD8+ cells) in all HCT cohorts.

  • Fig. 2.

    PCA and DE show that the T cell transcriptome recapitulates clinical observations. (A) First and second principal component (PC) projections reveal clustering of transplanted animals by immunoprophylactic strategy and donor source. Each dot represents an array sample, the center of inertia ellipses corresponds to the mean projections of the group, and the shaded area represents the 90% confidence interval. (B) Weighted Venn diagrams showing the number of transcripts differentially expressed in all allogeneic transplants compared to healthy controls (left) and compared to autologous controls (right). Overlapping areas in the Venn diagrams indicate over- or underrepresented transcripts that are shared between the cohorts. The number of transcripts is indicated for each comparative group. Differential expression (DE) analysis used a significance threshold of 0.05 derived from an empirical Bayes moderated t statistic, corrected for multiple hypothesis testing using the Benjamini-Hochberg procedure. An absolute fold-change threshold of 1.4 was used. (C) Chow-Ruskey Venn diagram (weighted) showing all expression data compared to the No Rx cohort. (D) Weighted Venn diagram showing the Auto cohort and untransplanted healthy controls versus the No Rx cohort.

  • Fig. 3. Top 200 differentially expressed transcripts from CD3+ T cells during GVHD reveal that cell turnover is a dominant expression theme during GVHD.

    Heat map of top 100 overrepresented and top 100 underrepresented transcripts (200 total) sorted on absolute fold change observed in the comparison between the No Rx and autologous transplant cohorts. Transcripts were categorized into six functional categories: (i) cytokines, chemokines, and their receptors (yellow), (ii) cell turnover (green), (iii) activation or effector function of T cells (magenta), (iv) T regulatory cells (light blue), (v) other (dark green), and (vi) no annotation available (white).

  • Fig. 4. GSEA identifies T cell immune dysregulation and proliferation as pathophysiologic drivers of primate GVHD.

    (A) GSEA was performed as previously described (24, 25). Shown are representative enrichment plots of immune-related gene sets overrepresented in the No Rx allogeneic versus autologous transplant cohorts. The false discovery rate (FDR) q value for the observed enrichment score was derived using a null distribution of enrichment scores generated from 1000 permutations of gene set labels. **FDR q value <0.001; *FDR q value <0.05 but >0.001. Nonsignificant FDR q value >0.05. (B) Representative gene sets related to proliferation and cell cycle control, with statistics computed as in (A). (C) Representative LCMV-associated gene sets, with statistics computed as in (A).

  • Fig. 5. Gene expression data from NHP with aGVHD are consistent with recent therapeutic strategies for GVHD prophylaxis and with T cell biomarkers for GVHD.

    (A) Box plots of expression data for individual transcripts recently being investigated as pharmacologic targets for GVHD prophylaxis (CCR5, CD2, IL-12RB2, and CD38) or as potential biomarkers [IL-2RA, HAVCR2 (TIM3), IL-10, CXCL10, and TNFRSF1B] in NHP HCT cohorts and healthy controls. Horizontal significance bars denote comparisons with a moderated t statistic <0.05 corrected for multiple hypothesis testing using Benjamini-Hochberg procedure. (B) GSEA was performed comparing No Rx (left, red), Tac-Mtx (left, green), and Siro (left, blue) to Auto (right) cohorts. No Rx and Siro cohorts have a significant enrichment of transcripts previously shown to be up-regulated in TH1 relative to TH17 (38). Hypothesis testing was performed as described in Fig. 4A. (C) Box plots of expression data for S100A8 and S100A9 in NHP HCT cohorts and healthy controls. Significance bars and color groups are as described in Fig. 5A, above. (D) Expression data (left) and longitudinal flow cytometric analysis (right) of granzyme B and Ki-67 expression on CD4+ and CD8+ in NHP HCT allotransplant cohorts and controls. Horizontal bars denote significance as described in Fig. 5A.

  • Fig. 6. Transcriptional analysis of primate GVHD pathophysiology.

    (A) Box plot of expression data from NOTCH1 (top) and a heat map showing z score genetic expression of the NOTCH interactome (bottom) in NHP HCT cohorts and healthy controls. The NOTCH interactome was constructed using the Ingenuity software and was interrogated for differentially expressed transcripts that were significantly over- or underrepresented in the No Rx cohort relative to the Auto cohort. (B) Heat map showing z score of transformed genetic expression data for cell death pathways in NHP HCT cohorts and healthy controls. Innermost row side colors indicate whether the gene has been shown to positively regulate (dark red) or inhibit (light blue) cell death. Outermost row side colors indicate the classification of the mechanism of cell death most associated with each gene product. The gene list was generated by interrogating DeathBase (51) for transcripts that were differentially expressed (adjusted P value <0.05) between the No Rx cohort and HC. Classification data were also taken from DeathBase.

  • Fig. 7.

    Analysis of over- or underrepresented transcripts during GVHD reveals enrichment of AURKA interactomes. (A) Box plots of expression data for four mitotic spindle–associated transcripts (AURKA, KPNA2, RAN, and KIF15) that show enrichment in No Rx cohort relative to autologous controls. Horizontal significance bars denote comparisons with a moderated t statistic <0.05 corrected for multiple hypothesis testing using Benjamini-Hochberg procedure. (B) Enrichment of the AURKA interactome in the leading edge of three gene lists ranked by expression differences between (i) No Rx and Auto (red traces), (ii) Siro and Auto (blue traces), and (iii) Tac-Mtx and Auto (green traces). The AURKA interactome was created using IPA. Hypothesis testing was performed as in Fig. 4A. (C) Heat map showing z score of transformed genetic expression data for transcripts in the leading edge of the AURKA GSEA enrichment plot (B, above) from all animals.

  • Fig. 8. AURKA inhibition represents a rational target for GVHD inhibition.

    (A) GVHD clinical score from transplant recipients: mice were weighed and monitored for clinical signs of GVHD, with scores based on weight loss, posture, activity, fur texture, and skin integrity twice weekly as previously described (82). MLN8237 (aurora kinase inhibitor; blue lines) was given by gavage daily at a dose of 30 mg/kg. Treatment with MLN8237 was begun on day 0 and was continued through day 30. (B) Percent survival of mice treated with MLN8237. The Kaplan-Meier product-limit method was used to calculate survival. Differences between groups were determined using log-rank statistics. **P = 0.0007 for MLN8237 versus vehicle. (C) Allo-proliferating CD4 and CD8 T cells in a mixed lymphocyte reaction (MLR) exhibit significant enrichment of AURKA relative to nonproliferating cells. Bulk T cells were isolated from healthy donors and allowed to proliferate on T cell–depleted irradiated peripheral blood mononuclear cells (PBMCs) from an allogeneic donor. On day 4 of the MLR, T cells were harvested and sorted for CD4 and CD8 expression and proliferative status. Absolute AURKA expression was measured using droplet digital polymerase chain reaction (PCR) and normalized to β-2 microglobulin (B2M) expression. *P < 0.05 using paired t test. (D) Transcriptome profiling was performed on sorted T cells from 11 patients with aGVHD (GVHD; red) and 13 post-HCT patients without evidence of GVHD (No GVHD; white). Patients with GVHD showed significant enrichment of AURKA transcripts. Horizontal bar indicates significance to a level <0.05 using unpaired t test. CTV, CellTrace Violet.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/315/315ra191/DC1

    Fig. S1. Sorting strategy and purity of T cells destined for microarray analysis.

    Fig. S2. Successful batch effect correction for PCA.

    Fig. S3. GSEA performed using previously published gene sets related to LCMV infection reveals commonality between T cell activation in response to alloactivation and acute viral infection.

    Fig. S4. Selected dysregulation of TH17-associated transcripts.

    Fig. S5. AURKA interactome shows little overlap with the leading edge genes in the LCMV GSEA analyses.

    Fig. S6. Translational pipeline for GVHD pathway discovery.

    Fig. S7. Investigation of SHH pathway enrichment in GVHD

    Table S1. Transplant cell doses for autologous and allogeneic transplant recipients.

    Table S2. Top gene analysis—Top 200 over- or underrepresented transcripts during GVHD in NHP: Comparison between the No Rx and autologous controls.

    Table S3. GSEA results—Gene sets enriched in No Rx versus autologous controls.

    Table S4. DAVID pathway analysis of the combined leading edge genes found in the No Rx versus autologous controls from the LCMV gene sets depicted in Fig. 4C and fig. S3.

    Table S5. DAVID pathway analysis of transcripts differentially expressed between the No Rx and autologous controls.

    Table S6. Clinical characteristics of transplant patients.

    Data S1. Spreadsheet with raw NHP clinical score data, NHP survival data, NHP chimerism data, murine clinical score data, murine survival data, and human MLR gene expression data (Figs. 1, B to D, and 8, A to C).

    Data S2. Spreadsheet with raw flow cytometric data from Figs. 1E and 5D.

  • Supplementary Material for:

    Transcriptome analysis of GVHD reveals aurora kinase A as a targetable pathway for disease prevention

    Scott N. Furlan, Benjamin Watkins, Victor Tkachev, Ryan Flynn, Sarah Cooley, Swetha Ramakrishnan, Karnail Singh, Cindy Giver, Kelly Hamby, Linda Stempora, Aneesah Garrett, Jingyang Chen, Kayla M. Betz, Carly G. K. Ziegler, Gregory K. Tharp, Steven E. Bosinger, Daniel E. L. Promislow, Jeffrey S. Miller, Edmund K. Waller, Bruce R. Blazar, Leslie S. Kean*

    *Corresponding author. E-mail: leslie.kean{at}seattlechildrens.org

    Published 25 November 2015, Sci. Transl. Med. 7, 315ra191 (2015)
    DOI: 10.1126/scitranslmed.aad3231

    This PDF file includes:

    • Fig. S1. Sorting strategy and purity of T cells destined for microarray analysis.
    • Fig. S2. Successful batch effect correction for PCA.
    • Fig. S3. GSEA performed using previously published gene sets related to LCMV infection reveals commonality between T cell activation in response to alloactivation and acute viral infection.
    • Fig. S4. Selected dysregulation of TH17-associated transcripts.
    • Fig. S5. AURKA interactome shows little overlap with the leading edge genes in the LCMV GSEA analyses.
    • Fig. S6. Translational pipeline for GVHD pathway discovery.
    • Fig. S7. Investigation of SHH pathway enrichment in GVHD Table S1. Transplant cell doses for autologous and allogeneic transplant recipients.
    • Legend for tables S2 to S5
    • Table S6. Clinical characteristics of transplant patients.

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S2 (Microsoft Excel format). Top gene analysis—Top 200 over- or underrepresented transcripts during GVHD in NHP: Comparison between the No Rx and autologous controls.
    • Table S3 (Microsoft Excel format). GSEA results—Gene sets enriched in No Rx versus autologous controls.
    • Table S4 (Microsoft Excel format). DAVID pathway analysis of the combined leading edge genes found in the No Rx versus autologous controls from the LCMV gene sets depicted in Fig. 4C and fig. S3.
    • Table S5 (Microsoft Excel format). DAVID pathway analysis of transcripts differentially expressed between the No Rx and autologous controls.
    • Data S1 (Microsoft Excel format). Spreadsheet with raw NHP clinical score data, NHP survival data, NHP chimerism data, murine clinical score data, murine survival data, and human MLR gene expression data (Figs. 1, B to D, and 8, A to C).
    • Data S2 (Microsoft Excel format). Spreadsheet with raw flow cytometric data from Figs. 1E and 5D.

    [Download Tables S2 to S5]

    [Download Data S1 and S2]

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