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DNA methylation predicts infection risk in kidney transplant recipients

Fei-Man Hsu, Harry Pickering, Liudmilla Rubbi, Michael Thompson, Elaine F Reed, Matteo Pellegrini  Correspondence email, Joanna M Schaenman  Correspondence email
Published 5 May 2025. DOI: 10.26508/lsa.202403124

Article Figures & Data

Figures

  • Figure 1.
    Figure 1. Study overview.

    (A) Schematic workflow of sample collection. Blood was collected before KTx at day 0 (pre-Tx) and then at day 90 (post-Tx). Data on infection and rejection during the first year after KTx were collected. (B) DNA methylation PCA color-coded with the demographic and clinical traits. CMV indicates negative or positive recipient serostatus, Transplant indicates pre- or post-Tx, ATG indicates whether patients received ATG at the time of sampling, and infection risk indicates those who did or did not experience infection in the first year after transplant. (C) Correlation matrix of the top 5 DNA methylation PCs with each trait.

  • Figure 2.
    Figure 2. ATG induction reduces CD4 T-cell population.

    (A) Cell composition estimated by DNA methylation. (B) Cell composition measured by flow cytometry.

  • Figure S1.
    Figure S1. Supporting data for TBS-seq cell-type deconvolution.

    (A) Differentially methylated regions used to deconvolution each cell type. (B) Correlation matrix of DNA methylation–estimated and flow cytometry T-cell composition.

  • Figure S2.
    Figure S2. Penalized logistic regression models.
  • Figure 3.
    Figure 3. Multivariate multiple linear regression model.

    (A) Spearman correlation matrix of actual-prediction traits. (B) Distribution of predicted values of ATG (left) and Infection Risk (right). (C) ROC curves of ATG (left) and Infection Risk (right). (D) Manhattan plot shows the 515 hyper-methylated CpG sites with ATG treatment. (E) Gene ontology of genes covered by the 515 hyper-methylated CpG sites with ATG treatment.

  • Figure S3.
    Figure S3. ATG-associated hypermethylation on ZBTB7B.
  • Figure 4.
    Figure 4. Time-to-infection survival analyses with DNA methylation–estimated covariates.

    (A) Epi-infection score from the MMLR model is statistically associated with time to infection. (B) Epi-ATG score is not associated with time to infection.

  • Figure S4.
    Figure S4. Time-to-infection survival analysis with induction therapies (ATG/SIMULECT) from the MMLR model.
  • Figure S5.
    Figure S5. Moderation analyses of epigenetic acceleration.

    (A, B) Transplant (A) and ATG treatment (B) accelerate epigenetic aging with statistical significance. (C, D) Infection Risk (C) and CMV serostatus (D) are not statistically significant moderators.

  • Figure S6.
    Figure S6. RNA-seq principal component analysis.

    (A) Correlation matrix of the top 5 gene expression PCs with traits. (B) Color-coded PCA scatter plots of each trait.

  • Figure 5.
    Figure 5. ATG induction alters gene expression.

    (A) Differential gene expression analysis of ATG induction. (B) GO analyses of up-regulated (left) and down-regulated (right) genes by ATG induction. (C, D) Transcriptomic MMLR model predicts ATG induction (C) but not Infection Risk (D).

  • Figure S7.
    Figure S7. ATG-associated hyper-methylated genes.

    (A, B) down-regulation and (B) up-regulation.

  • Figure S8.
    Figure S8. Supporting data for RNA-seq analyses.

    (A) Differential gene expression analysis pre- and post-Tx. (B) Venn diagram of ATG- and Transplant-associated DEGs. (C) Hierarchical clustering of RNA-seq with ATG-associated DEGs.

Tables

  • Table 1.

    Patient characteristics.

    All subjectsTBS-seqRNA-seq
    PrePostPre/Post
    n (%)9072 (80)50 (56)37/37 (41)
    Age median [IQR]52 (41, 60)52 (40, 59)50 (41, 59)54 (42, 63)
    Sex, n (%)
     Male55 (61)43 (60)30 (60)24 (65)
     Female35 (39)29 (40)20 (40)13 (35)
    CMV, n (%)
     Positive65 (72)49 (68)40 (80)23 (62)
     Negative25 (28)23 (32)10 (20)14 (38)
    First transplant, n (%)78 (87)62 (86)43 (86)34 (92)
    Donor type, n (%)
     Deceased77 (86)60 (83)39 (78)37 (100)
     Live13 (14)12 (17)11 (22)0 (0)
    Infection, n (%)45 (50)34 (47)24 (48)23 (62)
    Induction, n (%)
     ATG60 (67)44 (61)23 (46)30 (82)
     SIMULECT30 (33)28 (39)27 (54)7 (19)
    Maintenance, n (%)
     TAC86 (96)69 (96)48 (96)34 (92)
     BELA4 (4)3 (4)2 (4)3 (8)
    Acute rejection, n (%)11 (12)9 (13)7 (14)3 (8)
    Death, n (%)0 (0)0 (0)0 (0)0 (0)
  • Table 2.

    Top enriched pathways of gene expression through ATG-induced DNA methylation alterations.

    PathwayPGenesDirection
    Response to Retinoic Acid (GO:0032526)1.44 × 10−4LTK; RARA; PTK6Down-regulation
    Regulation of I-kappaB Kinase/NF-kappaB Signaling (GO:0043122)6.23 × 10−4EDAR; TNFRSF25; TRAF1; LTB
    Positive Regulation of Interleukin-12 Production (GO:0032735)1.43 × 10−3IL-23A; LTB
    Positive Regulation of Cytokine Production (GO:0001819)2.04 × 10−3CD6; IL-23A; RARA; LTB
    Positive Regulation of T Cell Proliferation (GO:0042102)5.46 × 10−3CD6; IL-23A
    Regulation of Defense Response To Virus by Host (GO:0050691)3.93 × 10−4APOBEC3G; IFNLR1Up-regulation
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DNA methylation in kidney transplant patients
Fei-Man Hsu, Harry Pickering, Liudmilla Rubbi, Michael Thompson, Elaine F Reed, Matteo Pellegrini, Joanna M Schaenman
Life Science Alliance May 2025, 8 (7) e202403124; DOI: 10.26508/lsa.202403124
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