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Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology.

Code ↔ Paper

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  1. [1] § Results › Tau and Aβ-associated DNA methylation changes are more pronounced in the hippocampus ↔ 0_PaperFigs/MainFigures.R, lines 89–129 · score 0.82 · Dennd1a, Mef2c, top ranked DMPs, Agbl5, Mctp1, Cetn3
  2. [2] § Methods › Reduced representation bisulfite sequencing (RRBS) ↔ 1_RRBS/J20/1_bismark.sh, the whole file · a weak match · score 0.82 · merge_non_CpG, coverage2cytosine, Bismark, Bowtie2, trimmed, discordance
  3. [3] § Methods › Reduced representation bisulfite sequencing (RRBS) ↔ 1_RRBS/J20/1_bismark_rerun.sh, the whole file · a weak match · score 0.82 · merge_non_CpG, coverage2cytosine, Bismark, Bowtie2, trimmed, discordance
  4. [4] § Results › DNA methylation changes in rTg4510 cortex are annotated to genes regulating neuronal function and apoptosis ↔ 0_PaperFigs/MainFigures.R, lines 1–41 · score 0.79 · Insyn2b, Creb3l4, As3mt, gene tracks, top ranked, Adk
  5. [5] § Results › Tau and Aβ-associated DNA methylation changes are more pronounced in the hippocampus ↔ 0_PaperFigs/MainFigures.R, lines 89–129 · score 0.77 · Dennd1a, Mef2c, top ranked DMPs, Meis2, Pxk, Rapgefl1
  6. [6] § Methods › Differential DNA methylation analysis ↔ 1_RRBS/functions/BetaRegressionDMPs.R, lines 1–6 · score 0.69 · likelihood ratio, beta regression, LRT, RRBS, DMPs
  7. [7] § Methods › Differential DNA methylation analysis ↔ 1_RRBS/functions/chipSeekerAnnotation.R, lines 20–65 · score 0.68 · tssRegion, annoDb, mm, ChIPseeker, intron, positions
  8. [8] § Methods › Comparative analysis with human data ↔ 4_HumanGeneListComparisons/2_convertGeneList.R, the whole file · a weak match · score 0.66 · homologous genes, mouse gene, human AD, UCSC, meta, methylated
  9. [9] § Methods › Differential DNA methylation analysis ↔ 1_RRBS/rTg4510/4_annnotate_DMP.R, the whole file · a weak match · score 0.65 · ChipSeeker, mouse reference, methylated positions, GENCODE, intron, RRBS
  10. [10] § Results › Tau and Aβ-associated DNA methylation changes are more pronounced in the hippocampus ↔ 0_PaperFigs/Stats.R, lines 162–232 · score 0.64 · Mef2c, Cetn3, Mir568, Sox4, Meis2, Pxk
  11. [11] § Results › Tau and Aβ-associated DNA methylation changes are more pronounced in the hippocampus ↔ 0_PaperFigs/SupplementaryFigures.R, lines 44–130 · score 0.63 · Cltc, chr17, Satb1, Ncapg2, chr12, Fgf14
  12. [12] § Methods › Mammalian methylation array ↔ 2_Array/1_preprocessing/1_QC_Mouse_Array.Rmd, lines 529–582 · score 0.61 · sex probe, reported sex, mismatch, QC, predicted, mouse
  13. [13] § Results › DNA methylation changes in rTg4510 cortex are annotated to genes regulating neuronal function and apoptosis ↔ 0_PaperFigs/MainFigures.R, lines 1–41 · score 0.61 · Insyn2b, top ranked DMP, Adk, Cisd3, Zfp423, AD mice
  14. [14] § Results › DNA methylation changes in rTg4510 cortex are annotated to genes regulating neuronal function and apoptosis ↔ 0_PaperFigs/Stats.R, lines 248–274 · score 0.57 · Creb3l4, As3mt, Arsi, chr12, Fgf14, Mapt
  15. [15] § Results › DNA methylation changes in J20 cortex are annotated to genes involved in mitochondrial homeostasis ↔ 9_methylation_expression_integration/2_simpleCorrelation.R, lines 44–84 · score 0.57 · RNA seq, J20 genotype, gene expression, S4, S5, rTg4510
  16. [16] § Results › DNA methylation changes in J20 cortex are annotated to genes involved in mitochondrial homeostasis ↔ 0_PaperFigs/MainFigures.R, lines 44–86 · score 0.56 · gene tracks, Top ranked, Fgfr2, Manhattan, Nutf2, Tenm2

Paper

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The authors' code

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  1. #!/usr/bin/env Rscript
  2. ## ----------Script-----------------
  3. ##
  4. ## Purpose: Supplementary Figures
  5. ##
  6. ##
  7. ## Author: Szi Kay Leung ([email hidden])
  8. ##
  9. ## ---------- Notes -----------------
  11. #-------------- Input -------------
  13. scriptDir = "C:/Users/sl693/OneDrive - University of Exeter/ExeterPostDoc/2_Scripts/AD_mouse_methylation/"
  14. source(paste0(scriptDir, "0_PaperFigs/paper_import.config.R"))
  16. ## ------------ Figure 1: Overview -------
  17. # generated via ppt
  19. ## ------------ Figure 2: rTg4510 -------
  21. # Figure 2A: Manhattan plot of rtg4510 genotype (export plots as png (width = 1200, height = 260))
  22. pManhattan <- list()
  23. pManhattan$rTg4510_Genotype <- plot_manhattan(rTg4510_rrbs_results$Genotype, rTg4510_array_results$Genotype, mode = "Genotype")
  24. pManhattan$rTg4510_Genotype
  26. # Figure 2B: top-ranked DMPs in rTg4510 genotype
  27. Dcaf5 <- plotGeneTrackDMP(sigRes$rTg4510$Genotype, sigBeta$rTg4510$Genotype, phenotype$rTg4510, "Dcaf5", "ENSMUST00000054145.7", boxplot = TRUE, colour = "rTg4510")
  28. Arsi <- plotGeneTrackDMP(sigRes$rTg4510$Genotype, sigBeta$rTg4510$Genotype, phenotype$rTg4510, "Arsi", "ENSMUST00000040359.5", colour = "rTg4510")
  29. Creb3l4 <- plotGeneTrackDMP(sigRes$rTg4510$Genotype, sigBeta$rTg4510$Genotype, phenotype$rTg4510, "Creb3l4", "ENSMUST00000029547.9", boxplot = TRUE, colour = "rTg4510")
  30. As3mt <- plotGeneTrackDMP(sigRes$rTg4510$Genotype, sigBeta$rTg4510$Genotype, phenotype$rTg4510, "As3mt", "ENSMUST00000003655.8", colour = "rTg4510")
  32. # Figure 2C: Manhattan plot of rTg4510 pathology (export plots as png (width = 1200, height = 260))
  33. pManhattan$rTg4510_Pathology <- plot_manhattan(rTg4510_rrbs_results$Pathology, rTg4510_array_results$Pathology, mode = "Pathology")
  34. pManhattan$rTg4510_Pathology
  36. # Figure 2D: top-ranked DMPs in rTg4510 pathology
  37. Insyn2b <- plotGeneTrackDMP(sigRes$rTg4510$Pathology, sigBeta$rTg4510$Pathology, phenotype$rTg4510, "Insyn2b", "ENSMUST00000165963.8", colour = "rTg4510", boxplot = TRUE, pathology = TRUE)
  38. Zfp423 <- plotGeneTrackDMP(sigRes$rTg4510$Pathology, sigBeta$rTg4510$Pathology, phenotype$rTg4510, "Zfp423", "ENSMUST00000109655.8", colour = "rTg4510", boxplot = TRUE, pathology = TRUE, position = "chr8:87750175")
  39. Ankrd52 <- plotGeneTrackDMP(sigRes$rTg4510$Pathology, sigBeta$rTg4510$Pathology, phenotype$rTg4510, "Ankrd52", "ENSMUST00000014642.9", colour = "rTg4510", boxplot = TRUE, pathology = TRUE)
  40. Adk <- plotGeneTrackDMP(sigRes$rTg4510$Pathology, sigBeta$rTg4510$Pathology, phenotype$rTg4510, "Adk", "ENSMUST00000045376.10", colour = "rTg4510", boxplot = TRUE, pathology = TRUE)
  41. Cisd3 <- plotGeneTrackDMP(sigRes$rTg4510$Pathology, sigBeta$rTg4510$Pathology, phenotype$rTg4510, "Cisd3", "ENSMUST00000107584.7", colour = "rTg4510", boxplot = TRUE, pathology = TRUE)
  44. ## ------------ Figure 3: J20 -------
  46. # Figure 3A: Manhattan plot of J20 genotype (export plots as png (width = 1200, height = 260))
  47. pManhattan$J20_Genotype <- plot_manhattan(J20_rrbs_results$Genotype, J20_array_results$Genotype, mode = "Genotype")
  48. pManhattan$J20_Genotype
  50. # Figure 3B: top-ranked DMPs in J20 genotype
  51. Nutf2 <- plotGeneTrackDMP(sigRes$J20$Genotype, sigBeta$J20$Genotype, phenotype$J20, "Nutf2", "ENSMUST00000008594.8", colour = "J20", boxplot = TRUE)
  52. Tenm2 <- plotGeneTrackDMP(sigRes$J20$Genotype, sigBeta$J20$Genotype, phenotype$J20, "Tenm2", "ENSMUST00000102801.7", colour = "J20", boxplot = TRUE)
  54. # Figure 3C: Manhattan plot of J20 pathology (export plots as png (width = 1200, height = 260))
  55. pManhattan$J20_Pathology <- plot_manhattan(J20_rrbs_results$Pathology, J20_array_results$Pathology, mode = "Pathology")
  56. pManhattan$J20_Pathology
  58. # Figure 3D: top-ranked DMPs in J20 pathology
  59. Grk2 <- plotGeneTrackDMP(sigRes$J20$Pathology, sigBeta$J20$Pathology, phenotype$J20, "Grk2", "ENSMUST00000167511.2", colour = "J20", boxplot = TRUE, pathology = TRUE)
  60. Fgfr2 <- plotGeneTrackDMP(sigRes$J20$Pathology, sigBeta$J20$Pathology, phenotype$J20, "Fgfr2", "ENSMUST00000117073.1", colour = "J20", boxplot = TRUE, pathology = TRUE)
  61. Ncam2 <- plotGeneTrackDMP(sigRes$J20$PathologyCommonInteraction, sigBeta$J20$Pathology, phenotype$J20, "Ncam2", "ENSMUST00000037785.13", colour = "J20", pathology = TRUE, boxplot = TRUE)
  62. Zmiz1 <- plotGeneTrackDMP(sigRes$J20$PathologyCommonInteraction, sigBeta$J20$Pathology, phenotype$J20, "Zmiz1", "ENSMUST00000162645.7", colour = "J20", boxplot = TRUE, pathology = TRUE)
  65. ## ------------ Figure 4: ECX vs HIP -------
  67. # Figure 4A: Venn diagram of hippocampus vs entorhinal cortex
  68. HipECXVennrTg4510 <- plot_grid(venn.diagram(
  69. x = list(rTg4510_array_sig$ECX$Genotype$position, rTg4510_array_sig$ECX$Pathology$position,
  70. rTg4510_array_sig$HIP$Genotype$position, rTg4510_array_sig$HIP$Pathology$position),
  71. category.names = c("ECX_Genotype" , "ECX_Pathology", "HIP_Genotype", "HIP_Pathology"),
  72. fill = pastelColours,
  73. cex = 0.9,
  74. cat.cex = 0.9,
  75. filename = NULL
  76. ))
  78. HipECXVennJ20 <- plot_grid(venn.diagram(
  79. x = list(J20_array_sig$ECX$Genotype$position, J20_array_sig$ECX$Pathology$position,
  80. J20_array_sig$HIP$Genotype$position, J20_array_sig$HIP$Pathology$position),
  81. category.names = c("ECX_Genotype" , "ECX_Pathology", "HIP_Genotype", "HIP_Pathology"),
  82. fill = pastelColours,
  83. cex = 0.9,
  84. cat.cex = 0.9,
  85. filename = NULL
  86. ))
  89. # Figure 4B: Top-ranked DMP across rTg4510 ECX and HIP
  90. Dennd1a = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  91. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr2:37946161",
  92. pathology = TRUE, gene = "Dennd1a")
  94. Rapgefl1 = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  95. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr11:98838683",
  96. pathology = TRUE, gene = "Rapgefl1")
  99. # Figure 4C: Top-ranked DMP across rTg4510 HIP but not ECX
  100. HIPrTg4510plots <- list(
  101. Pxk = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  102. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr14:8146212",
  103. gene = "Pxk"),
  104. Mef2c = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  105. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr13:83504232",
  106. gene = "Mef2c"),
  107. Agbl5 = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  108. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr5:30890202", pathology = TRUE,
  109. gene = "Agbl5"),
  110. Meis2 = plot_DMP_byTissue(ECXbetaMatrix=rTg4510_array_beta, HIPbetaMatrix=rTg4510_array_HIP_beta,
  111. ECXphenotypeFile=phenotype$rTg4510, HIPphenotypeFile=phenotype$rTg4510_HIP, position ="chr2:116018971", pathology = TRUE,
  112. gene = "Meis2")
  113. )
  115. # Figure 4D: Top-ranked DMP across J20 HIP but not ECX
  116. HIPJ20plots <- list(
  117. Mir568 = plot_DMP_byTissue(ECXbetaMatrix=J20_array_beta, HIPbetaMatrix=J20_array_HIP_beta,
  118. ECXphenotypeFile=phenotype$J20, HIPphenotypeFile=phenotype$J20_HIP, position ="chr16:43609394",
  119. gene = "Mir568", model = "J20"),
  120. Mctp1 = plot_DMP_byTissue(ECXbetaMatrix=J20_array_beta, HIPbetaMatrix=J20_array_HIP_beta,
  121. ECXphenotypeFile=phenotype$J20, HIPphenotypeFile=phenotype$J20_HIP, position ="chr13:76810803",
  122. gene = "Mctp1", model = "J20"),
  123. Sox4 = plot_DMP_byTissue(ECXbetaMatrix=J20_array_beta, HIPbetaMatrix=J20_array_HIP_beta,
  124. ECXphenotypeFile=phenotype$J20, HIPphenotypeFile=phenotype$J20_HIP, position ="chr13:28949481",
  125. gene = "Sox4", model = "J20", pathology = TRUE),
  126. Cetn3 = plot_DMP_byTissue(ECXbetaMatrix=J20_array_beta, HIPbetaMatrix=J20_array_HIP_beta,
  127. ECXphenotypeFile=phenotype$J20, HIPphenotypeFile=phenotype$J20_HIP, position ="chr13:81828611", pathology = TRUE,
  128. gene = "Cetn3", model = "J20")
  129. )
  132. ## ------------ Figure 5: Human comparison -------
  134. # Figure 5A: Venn diagram of rTg4510 vs J20 vs Human
  135. sigRes$rTg4510 <- lapply(sigRes$rTg4510, function(x) x %>% filter(ChIPseeker_GeneSymbol != "NA"))
  136. sigRes$J20 <- lapply(sigRes$J20, function(x) x %>% filter(ChIPseeker_GeneSymbol != "NA"))
  137. pHuman1 <- venn.diagram(
  138. x = list(c(sigRes$rTg4510$Genotype$ChIPseeker_GeneSymbol,sigRes$rTg4510$Pathology$ChIPseeker_GeneSymbol),
  139. c(sigRes$J20$Genotype$ChIPseeker_GeneSymbol, sigRes$J20$Pathology$ChIPseeker_GeneSymbol),
  140. humanAllGeneList),
  141. category.names = c("rTg4510","J20","Human"),
  142. fill = c(label_colour("rTg4510"), label_colour("J20"),"yellow"),
  143. filename = NULL
  144. )
  147. # Figure 5B: Ank1 RRBS and pyrosequencing
  148. pAnk1DMP <- plot_gene_track(betaMatrix=sigBeta$rTg4510$Genotype, phenotypeFile=phenotype$rTg4510,
  149. position = "chr8:23023192", gene="Ank1", transcript="ENSMUST00000110688.8", colour = "rTg4510")
  151. tAnk1DMP <- plot_DMP(betaMatrix=sigBeta$rTg4510$Genotype, phenotypeFile=phenotype$rTg4510,
  152. position = c("chr8:23023240","chr8:23023210","chr8:23023192"), table = TRUE) %>% mutate(method = "RRBS")
  154. ank1PyroPos <- c(
  155. `Pos1Meth` = "chr8:23023192",
  156. `Pos3Meth` = "chr8:23023240"
  157. )
  158. ank1PyroPosdf <- reshape2::melt(ank1PyroPos, value.name = "Position") %>% tibble::rownames_to_column(., var = "prnpPosition")
  160. tAnk1Pyro <- input_pyro$ank1 %>% dplyr::select(SAMPLE, Age, Group.ID, Pos1Meth, Pos3Meth) %>%
  161. reshape2::melt(id = c("Age","Group.ID","SAMPLE"), variable.name = "Position", value.name = "methylation") %>%
  162. mutate(Age = as.factor(stringr::str_remove(Age,"m"))) %>%
  163. mutate(Group.ID = factor(Group.ID, levels = c("WT","TG"))) %>%
  164. merge(., phenotype$rTg4510, by.x = "SAMPLE", by.y = 0)%>%
  165. merge(., reshape2::melt(ank1PyroPos, value.name = "position"), by.x = "Position", by.y = 0) %>%
  166. dplyr::rename("sample"= "Position") %>% mutate(method = "Pyrosequencing") %>%
  167. mutate(methylation = methylation/100)
  169. pAnk1PyroRRBS <- rbind(tAnk1DMP, tAnk1Pyro %>% dplyr::select(colnames(tAnk1DMP))) %>%
  170. mutate(method = factor(method, levels = c("RRBS","Pyrosequencing"))) %>%
  171. ggplot(., aes(x = Genotype, y = methylation, fill = Genotype)) + geom_boxplot(outlier.shape = NA) +
  172. geom_jitter(aes(colour = Genotype),width = 0.25, size = 2) +
  173. scale_fill_manual(values = c(alpha("black",0.2), color_Tg4510_TG),guide="none") +
  174. scale_colour_manual(values = c("black", color_Tg4510_TG),guide="none") +
  175. labs(x = "Genotype", y = "Methylation") +
  176. facet_nested(~ position + method) +
  177. mytheme +
  178. theme(panel.border = element_rect(fill = NA, color = "grey", linetype = "dotted"),
  179. panel.grid.major = element_blank(),
  180. panel.grid.minor = element_blank(),
  181. strip.background = element_blank())
  185. ## ------------ pdf outputs -------
  187. pdf(paste0(output, "Figures/rTg4510_DMPs.pdf"), width = 21, height = 12)
  188. plot_grid(Dcaf5, Arsi, Creb3l4, As3mt, scale = 0.95)
  189. plot_grid(Cisd3, Zfp423, Adk, Insyn2b, scale = 0.95)
  190. dev.off()
  192. pdf(paste0(output, "Figures/J20_DMPs_2B.pdf"), width = 21, height = 8)
  193. plot_grid(Nutf2, Tenm2, scale = 0.95)
  194. dev.off()
  196. pdf(paste0(output, "Figures/J20_DMPs_2D.pdf"), width = 21, height = 12)
  197. plot_grid(Grk2, Fgfr2, Ncam2, Zmiz1, scale = 0.95)
  198. dev.off()
  200. pdf(paste0(output, "Figures/venn_HIP_ECX.pdf"), width = 10, height = 5)
  201. plot_grid(HipECXVennrTg4510,HipECXVennJ20, scale = 0.85)
  202. dev.off()
  204. pdf(paste0(output, "Figures/rTg4510_ECX_HIP.pdf"), width = 21, height = 4)
  205. plot_grid(Dennd1a, Rapgefl1, scale = 0.95, labels = c("i","ii"))
  206. dev.off()
  208. pdf(paste0(output, "Figures/rTg4510_HIP_notECX.pdf"), width = 21, height = 8)
  209. plot_grid(plotlist = HIPrTg4510plots, scale = 0.95, labels = c("i","ii","iii","iv"), label_size = 18)
  210. dev.off()
  212. pdf(paste0(output, "Figures/J20_HIP_notECX.pdf"), width = 21, height = 8)
  213. plot_grid(plotlist = HIPJ20plots, scale = 0.95, labels = c("i","ii","iii","iv"))
  214. dev.off()
  216. pdf(paste0(output, "Figures/Venn_human_comp.pdf"), width = 5, height = 5)
  217. plot_grid(pHuman1, scale = 0.95)
  218. dev.off()
  220. pdf(paste0(output, "Figures/Ank1.pdf"), width = 12, height = 8)
  221. plot_grid(pAnk1DMP, pAnk1PyroRRBS, ncol = 1, rel_heights = c(0.3,0.7))
  222. dev.off()

MainFigures.R at commit 9b016f9, no license · at the source

Overview

Authors: Szi Kay Leung1, Emma M. Walker1, Stefania Policicchio1,2, Aisha Dahir1, Dorothea Seiler Vellame1,3, Adam R. Smith1, Rhian Swarbrick1, Katie Lunnon1, Emma L. Dempster1, Zeshan Ahmed4, Eilis Hannon1, Isabel Castanho1,5,6, Jonathan Mill1
  1. Department of Clinical and Biomedical Sciences, University of Exeter,Exeter, UK
  2. Istituto Italiano di Tecnologia,Genova, Italy
  3. Department of Research Software and Analytics, University of Exeter,Exeter, UK
  4. Eli Lilly, Seaport Innovation Centre,Boston, MA USA
  5. Department of Pathology, Beth Israel Deaconess Medical Center,Boston, MA USA
  6. Harvard Medical School,Boston, MA USA
Institutions: University of Exeter (United Kingdom); Italian Institute of Technology (Italy); Eli Lilly (United States) (United States); Beth Israel Deaconess Medical Center (United States); Harvard University (United States)
Journal: NPJ dementia, volume 2, issue 1, article 23
Dates: received 1 September 2025; accepted 25 February 2026; published online 7 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44400-026-00074-y · PMID 41958871 · PMCID PMC13056554 · OpenAlex W4412800263
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.28.666515
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics
Keywords: Diseases, Genetics, Molecular biology, Neurology, Neuroscience
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: UK Medical Research Council (MC_PC_14127); Alzheimer’s Research UK (ARUK-PG2018B-016); Alzheimer’s Society (231); Wellcome Trust (WT097835MF); RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (BB/K003240/1)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Alzheimer’s disease (AD) is characterized by progressive neurodegeneration driven by tau and amyloid-β (Aβ) pathology, although the underlying molecular mechanisms remain incompletely understood. Emerging evidence implicates altered DNA methylation (DNAm) in AD but comprehensive analyses in experimental models are limited. Here, we profile DNAm dynamics in two widely used transgenic mouse models of tau (rTg4510) and Aβ (J20) neuropathology, focusing on the entorhinal cortex and hippocampus. Using reduced representation bisulfite sequencing (RRBS) and methylation arrays across multiple disease stages, we identified widespread pathology-associated DNAm alterations in both models. Tau pathology in rTg4510 mice was associated with extensive DNAm remodeling at genes involved in neuronal plasticity, apoptosis, and lipid metabolism, including Dcaf5, Creb3l4, and As3mt. In contrast, J20 mice exhibited more modest changes, primarily at immune-related loci such as Grk2, Ncam2, and Prmt8. Tau-associated DNAm changes were more consistent across brain areas than those associated with Aβ pathology. Comparison with human AD DNAm datasets revealed overlapping DNAm differences, including hypermethylation at Ank1 and Prdm16 in rTg4510 mice. These findings provide robust evidence for early, pathology-associated epigenetic alterations in AD and highlight the utility of epigenomic profiling in transgenic models for identifying novel targets for early intervention in AD.

Reproduced under the paper's license (CC BY), from the paper cited above.

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SziKayLeung/AD_mouse_methylation

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 9b016f9f57495862944a320475c4112e1b05d24c, 6 March 2026
Languages: R (69), Shell (23)
Size: 107 files, 92 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (49 files), reshape2 (17 files), ggplot2 (16 files), glmmTMB (12 files), lmerTest (12 files), cowplot (6 files), ggpubr (4 files), pheatmap (3 files), clusterProfiler (2 files), data.table (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
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93 files
At the source: github.com/SziKayLeung/AD_mouse_methylation

Zenodo 15741353

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Evidence: files inventoried
Size: 8 files
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Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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At the source:

The paper's code and data availability statement is in the Data section.

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  • 16 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Raw mammalian methylation array data and RRBS FASTQ files for both rTg4510 and J20 models have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE246561. Intermediate files are available on Zenodo (10.5281/zenodo.15741353). All code supporting the results presented in this study is available at https://github.com/SziKayLeung/AD_mouse_methylation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 5 funders, 56 references.

Cite

This paper

Leung, S. K., Walker, E. M., Policicchio, S., Dahir, A., Vellame, D. S., Smith, A. R., Swarbrick, R., Lunnon, K., Dempster, E. L., Ahmed, Z., Hannon, E., Castanho, I., & Mill, J. (2026). Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology. NPJ dementia, 2(1), 23. https://doi.org/10.1038/s44400-026-00074-y

BibTeX

@article{leung2026methylomic,
author = {Leung, Szi Kay and Walker, Emma M. and Policicchio, Stefania and Dahir, Aisha and Vellame, Dorothea Seiler and Smith, Adam R. and Swarbrick, Rhian and Lunnon, Katie and Dempster, Emma L. and Ahmed, Zeshan and Hannon, Eilis and Castanho, Isabel and Mill, Jonathan},
title = {{Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology}},
journal = {NPJ dementia},
year = {2026},
month = apr,
volume = {2},
number = {1},
pages = {23},
publisher = {Springer Science+Business Media},
issn = {3005-1940},
doi = {10.1038/s44400-026-00074-y},
url = {https://doi.org/10.1038/s44400-026-00074-y},
pmid = {41958871},
pmcid = {PMC13056554}
}

RIS

TY - JOUR
AU - Leung, Szi Kay
AU - Walker, Emma M.
AU - Policicchio, Stefania
AU - Dahir, Aisha
AU - Vellame, Dorothea Seiler
AU - Smith, Adam R.
AU - Swarbrick, Rhian
AU - Lunnon, Katie
AU - Dempster, Emma L.
AU - Ahmed, Zeshan
AU - Hannon, Eilis
AU - Castanho, Isabel
AU - Mill, Jonathan
TI - Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology
T2 - NPJ dementia
J2 - NPJ Dement
PY - 2026
DA - 2026/04/07
VL - 2
IS - 1
SP - 23
SN - 3005-1940
PB - Springer Science+Business Media
DO - 10.1038/s44400-026-00074-y
UR - https://doi.org/10.1038/s44400-026-00074-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44400-026-00074-y",
"type": "article-journal",
"title": "Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology",
"container-title": "NPJ dementia",
"author": [
{
"family": "Leung",
"given": "Szi Kay"
},
{
"family": "Walker",
"given": "Emma M."
},
{
"family": "Policicchio",
"given": "Stefania"
},
{
"family": "Dahir",
"given": "Aisha"
},
{
"family": "Vellame",
"given": "Dorothea Seiler"
},
{
"family": "Smith",
"given": "Adam R."
},
{
"family": "Swarbrick",
"given": "Rhian"
},
{
"family": "Lunnon",
"given": "Katie"
},
{
"family": "Dempster",
"given": "Emma L."
},
{
"family": "Ahmed",
"given": "Zeshan"
},
{
"family": "Hannon",
"given": "Eilis"
},
{
"family": "Castanho",
"given": "Isabel"
},
{
"family": "Mill",
"given": "Jonathan"
}
],
"container-title-short": "NPJ Dement",
"volume": "2",
"issue": "1",
"page": "23",
"DOI": "10.1038/s44400-026-00074-y",
"PMID": "41958871",
"PMCID": "PMC13056554",
"ISSN": "3005-1940",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1038/s44400-026-00074-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
7
]
]
}
}

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