OSCR

Modulating alternative splicing of <i>MECP2</i> is a potential therapeutic strategy for Rett syndrome.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § RESULTS › Isoform switching corrects transcriptomic dysregulation in RTT neurons ↔ T158M_NGN2_RNAseq.R, lines 400–485 · score 0.91 · 0–25 %, 25–50 %, 50–75 %, 75–100 %, axis, G118E
  2. [2] § RESULTS › Isoform switching corrects transcriptomic dysregulation in RTT neurons ↔ G118E_NGN2_downstream_E2KO.R, lines 1–44 · score 0.65 · defined disease gene, disease genes rescued, FDR, duplication, NGN2, timepoints
  3. [3] § RESULTS › Isoform switching corrects transcriptomic dysregulation in RTT neurons ↔ G118E_NGN2_downstream_E2KO.R, lines 221–301 · score 0.61 · 25–50 %, 50–75 %, 75–100 %, disease gene, DEGs, G118E

Paper

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The authors' code

R · 425 lines · 29 KB · MIT · 2 matches

  1. #for NGN2-neurons
  2. #Pipeline initally written by Alexander J Trostle
  3. #code to define disease signature, bin the resuce percentage, visualization for disease gene rescue
  4. rm(list = ls())
  5. options(java.parameters = "-Xmx8g" )
  7. setwd("path_to_wd")
  8. library(ggplot2)
  9. library(ggrepel)
  10. library(readxl)
  11. library(fgsea)
  12. library(pheatmap)
  13. library(GSVA)
  14. library(gridExtra)
  15. library(limma)
  16. library(dplyr)
  17. library(data.table)
  18. library(gprofiler2)
  20. f=14
  21. l2fc <- 0
  22. FDR <- 0.01
  23. #GO_Padj <- 0.1
  25. #load and filter - get disease signature
  26. G118EvsWT_1mo <- read.delim("path_to_deseq2_contrast_matrix")
  27. G118EvsWT_1mo <- G118EvsWT_1mo[((!is.na(G118EvsWT_1mo$Gene_symbol)) & (!is.na(G118EvsWT_1mo$padj)) & (!duplicated(G118EvsWT_1mo$Gene_symbol))),]
  28. G118EvsWT_1mo_degs <- G118EvsWT_1mo[((G118EvsWT_1mo$padj < FDR) & (abs(G118EvsWT_1mo$log2FoldChange) > l2fc)),]
  29. G118EvsWT_2mo <- read.delim("path_to_deseq2_contrast_matrix")
  30. G118EvsWT_2mo <- G118EvsWT_2mo[((!is.na(G118EvsWT_2mo$Gene_symbol)) & (!is.na(G118EvsWT_2mo$padj)) & (!duplicated(G118EvsWT_2mo$Gene_symbol))),]
  31. G118EvsWT_2mo_degs <- G118EvsWT_2mo[((G118EvsWT_2mo$padj < FDR) & (abs(G118EvsWT_2mo$log2FoldChange) > l2fc)),]
  33. #using deseq normalized counts to crudely estimate the number of "rescued genes"
  34. #just going to average per condition, make a simple plot
  35. norm.counts <- read.delim("Norm_expr_filt_genes.txt")
  37. #load and format sample info
  38. sample.info = read.table("sampleinfo.txt",header=T,sep="")
  39. sample.info$Clone <- as.factor(sample.info$Clone)
  40. sample.info$Sample_ID <- paste0("X",sample.info$Sample_ID)
  41. all(sample.info$Sample_ID %in% colnames(norm.counts))
  42. norm.counts <- norm.counts[sample.info$Sample_ID]
  43. sample.info$Sample_ID == colnames(norm.counts)
  44. sample.info$GenotypeTime <- paste0(sample.info$Genotype,"_",sample.info$Timepoint)
  46. #1 month time point
  47. norm.counts_1mo <- norm.counts[(sample.info$Timepoint == "1_month")]
  48. #build an average per condition df
  49. avg_1mo <- data.frame(
  50. row.names = row.names(norm.counts_1mo),
  51. WT = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control"),]$Sample_ID)]),
  52. E2KO = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO"),]$Sample_ID)]),
  53. G118E = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E"),]$Sample_ID)]))
  55. #length(which((avg_1mo$E2KO > avg_1mo$WT) & (avg_1mo$E2KO < avg_1mo$G118E) | ((avg_1mo$E2KO < avg_1mo$WT) & (avg_1mo$E2KO > avg_1mo$G118E))))
  56. #filter! to disease genes
  57. dim(G118EvsWT_1mo_degs)[1]
  58. avg_1mo_disease <- avg_1mo[(row.names(avg_1mo) %in% G118EvsWT_1mo_degs$Gene_ID),]
  59. length(which((avg_1mo_disease$E2KO < avg_1mo_disease$WT) & (avg_1mo_disease$E2KO > avg_1mo_disease$G118E)))
  60. length(which((avg_1mo_disease$E2KO > avg_1mo_disease$WT) & (avg_1mo_disease$E2KO < avg_1mo_disease$G118E)))
  61. (length(which((avg_1mo_disease$E2KO < avg_1mo_disease$WT) & (avg_1mo_disease$E2KO > avg_1mo_disease$G118E))) + length(which((avg_1mo_disease$E2KO > avg_1mo_disease$WT) & (avg_1mo_disease$E2KO < avg_1mo_disease$G118E)))) / dim(G118EvsWT_1mo_degs)[1]
  62. # define rescue genes
  63. rescue_1mo <- avg_1mo_disease[((avg_1mo_disease$E2KO < avg_1mo_disease$WT) & (avg_1mo_disease$E2KO > avg_1mo_disease$G118E)) |
  64. ((avg_1mo_disease$E2KO > avg_1mo_disease$WT) & (avg_1mo_disease$E2KO < avg_1mo_disease$G118E)),]
  66. #check % of rescue
  67. #formula
  68. #abs(G118E-E2KO)/abs(G118E-WT)
  69. #will split this on up vs down and also try to plot non rescued also!! Worth knowing if many genes are dramatically wrong
  70. rescue_1mo$pct_rescue <- 100*abs((rescue_1mo$G118E - rescue_1mo$E2KO)/(rescue_1mo$G118E - rescue_1mo$WT))
  71. pa <- ggplot(rescue_1mo, aes(pct_rescue)) +
  72. geom_histogram(aes(y = after_stat(count / sum(count))),color = "#000000", fill = "#0099F8") +
  73. scale_y_continuous(labels = scales::percent)+
  74. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  75. theme_bw()+
  76. theme(text = element_text(size = f, face = "bold"))
  77. ggsave("1_month_rescuegenes_pct.pdf",plot = pa, device = "pdf", width = 4, height = 2.5, units = c("in"),dpi = 300, scale = 2)
  79. #more plots!!
  80. p1<- ggplot((rescue_1mo[order(rescue_1mo$pct_rescue),]), aes(y=pct_rescue,x=rownames(rescue_1mo))) +
  81. geom_point(aes(color=pct_rescue))+
  82. scale_color_gradient(low = "gray45", high="purple")+
  83. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",
  84. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Genes", y = "Percent 'Rescue'") +
  85. theme_classic()+
  86. geom_hline(yintercept = 50)
  87. theme(text = element_text(size = f, face = "bold"))
  89. colfunc <- colorRampPalette(c("gray95", "gray44"))
  90. col <- colfunc(15)
  91. colfunc <- colorRampPalette(c("gray45", "purple4"))
  92. col <- c(col,colfunc(15))
  93. p2<-ggplot(rescue_1mo, aes(pct_rescue)) +
  94. geom_histogram(color = "#000000",aes(y = after_stat(count / sum(count))), fill = col) +
  95. scale_y_continuous(labels = scales::percent)+
  96. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",
  97. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  98. theme_bw()+
  99. theme(text = element_text(size = f, face = "bold"))
  101. p3<-ggplot(rescue_1mo, aes(pct_rescue)) +
  102. geom_histogram(color = "#000000",aes(y = after_stat(count / sum(count))), fill = c(rep("gray50",15),rep("purple3",15))) +
  103. scale_y_continuous(labels = scales::percent)+
  104. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",
  105. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  106. theme_bw()+
  107. theme(text = element_text(size = f, face = "bold"))
  109. rescue_1mo$dir <- ifelse(rescue_1mo$WT > rescue_1mo$G118E,"down","up")
  110. p5<-ggplot() +
  111. geom_histogram(data=rescue_1mo[(rescue_1mo$dir=="up"),],color = "#000000", fill = "red", aes(x = pct_rescue,y = after_stat(count / sum(count))))+
  112. geom_histogram(data=rescue_1mo[(rescue_1mo$dir=="down"),],color = "#000000", fill = "blue", aes(x = pct_rescue,y = -after_stat(count / sum(count))))+
  113. theme_bw()+
  114. geom_vline(xintercept = 50,linetype="dotdash")+
  115. scale_y_continuous(labels = scales::percent)+
  116. labs(title = "Direction and Magnitude of Rescue", x = "Percent of Rescue", y = "Count",fill="Direction",
  117. subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",scaption = "abs(G118E-E2KO)/abs(G118E-WT)") +
  118. coord_flip()
  120. p6<- ggplot(rescue_1mo, aes(x=pct_rescue,y=1)) +
  121. geom_jitter(height = .5,alpha=.9)+
  122. geom_boxplot(alpha=.4,color="purple")+
  123. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",
  124. scaption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Genes") +
  125. theme_classic()+
  126. geom_vline(xintercept = 50,linetype="dotdash")+
  127. theme(text = element_text(size = f, face = "bold"),axis.text.y = element_blank())
  129. p7<-ggplot(rescue_1mo, aes(x=pct_rescue,y=1)) +
  130. geom_jitter(shape=21,height = .5,alpha=.9,aes(fill=dir))+
  131. scale_fill_manual(values=c("blue","red"))+
  132. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "1 Month Disease Signature Genes with Expression Rescue (1736/2900)",
  133. scaption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Genes") +
  134. theme_classic()+
  135. geom_vline(xintercept = 50,linetype="dotdash")+
  136. theme(text = element_text(size = f, face = "bold"),axis.text.y = element_blank(),legend.position = "bottom")
  138. grid.arrange(p1,p2,p3,p5,p6,p7,ncol=2)
  140. #check health splits
  142. #healthy
  143. avg_1mo_healthy <- data.frame(
  144. row.names = row.names(norm.counts_1mo),
  145. WT = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]),
  146. E2KO = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]),
  147. G118E = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]))
  149. #length(which((avg_1mo$E2KO > avg_1mo$WT) & (avg_1mo$E2KO < avg_1mo$G118E) | ((avg_1mo$E2KO < avg_1mo$WT) & (avg_1mo$E2KO > avg_1mo$G118E))))
  150. #filter! to disease genes
  151. dim(G118EvsWT_1mo_degs)[1]
  152. avg_1mo_healthy_disease <- avg_1mo_healthy[(row.names(avg_1mo_healthy) %in% G118EvsWT_1mo_degs$Gene_ID),]
  153. length(which((avg_1mo_healthy_disease$E2KO < avg_1mo_healthy_disease$WT) & (avg_1mo_healthy_disease$E2KO > avg_1mo_healthy_disease$G118E)))
  154. length(which((avg_1mo_healthy_disease$E2KO > avg_1mo_healthy_disease$WT) & (avg_1mo_healthy_disease$E2KO < avg_1mo_healthy_disease$G118E)))
  155. (length(which((avg_1mo_healthy_disease$E2KO < avg_1mo_healthy_disease$WT) & (avg_1mo_healthy_disease$E2KO > avg_1mo_healthy_disease$G118E))) + length(which((avg_1mo_healthy_disease$E2KO > avg_1mo_healthy_disease$WT) & (avg_1mo_healthy_disease$E2KO < avg_1mo_healthy_disease$G118E)))) / dim(G118EvsWT_1mo_degs)[1]
  157. #unhealthy
  158. avg_1mo_unhealthy <- data.frame(
  159. row.names = row.names(norm.counts_1mo),
  160. WT = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]),
  161. E2KO = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]),
  162. G118E = rowMeans(norm.counts_1mo[,(names(norm.counts_1mo) %in% sample.info[(sample.info$Genotype == "G118E" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]))
  164. #length(which((avg_1mo$E2KO > avg_1mo$WT) & (avg_1mo$E2KO < avg_1mo$G118E) | ((avg_1mo$E2KO < avg_1mo$WT) & (avg_1mo$E2KO > avg_1mo$G118E))))
  165. #filter! to disease genes
  166. dim(G118EvsWT_1mo_degs)[1]
  167. avg_1mo_unhealthy_disease <- avg_1mo_unhealthy[(row.names(avg_1mo_unhealthy) %in% G118EvsWT_1mo_degs$Gene_ID),]
  168. length(which((avg_1mo_unhealthy_disease$E2KO < avg_1mo_unhealthy_disease$WT) & (avg_1mo_unhealthy_disease$E2KO > avg_1mo_unhealthy_disease$G118E)))
  169. length(which((avg_1mo_unhealthy_disease$E2KO > avg_1mo_unhealthy_disease$WT) & (avg_1mo_unhealthy_disease$E2KO < avg_1mo_unhealthy_disease$G118E)))
  170. (length(which((avg_1mo_unhealthy_disease$E2KO < avg_1mo_unhealthy_disease$WT) & (avg_1mo_unhealthy_disease$E2KO > avg_1mo_unhealthy_disease$G118E))) + length(which((avg_1mo_unhealthy_disease$E2KO > avg_1mo_unhealthy_disease$WT) & (avg_1mo_unhealthy_disease$E2KO < avg_1mo_unhealthy_disease$G118E)))) / dim(G118EvsWT_1mo_degs)[1]
  172. #get gene symbols
  173. namedf <- G118EvsWT_1mo[,c(1,2)]
  174. names(namedf) <- c("Row.names","Gene_symbol")
  175. avg_1mo_disease$Row.names <- rownames(avg_1mo_disease)
  176. avg_1mo_disease <- merge(avg_1mo_disease,namedf,by="Row.names",all.x=T)
  177. rownames(avg_1mo_disease) <- avg_1mo_disease$Gene_symbol
  178. avg_1mo_disease$Row.names <- NULL
  179. avg_1mo_disease$Gene_symbol <- NULL
  181. #write
  182. write.table(rownames(avg_1mo_disease[((avg_1mo_disease$E2KO < avg_1mo_disease$WT) & (avg_1mo_disease$E2KO > avg_1mo_disease$G118E)) |
  183. ((avg_1mo_disease$E2KO > avg_1mo_disease$WT) & (avg_1mo_disease$E2KO < avg_1mo_disease$G118E)),]),"harini_1month_rescueGenes.txt" ,sep='\t', row.names=F, quote=F, col.names = F)
  186. #maybe try rank, idk
  187. #rankedbigavg_logcpm <- as.data.frame(t(apply(bigavg_logcpm, 1, rank)))
  189. cols <- colorRampPalette(c("white", "firebrick1", "firebrick4"))(30)
  190. pheatmap(as.matrix(log10(1+avg_1mo_disease)), scale="row", color = cols, annotation_col = NULL, annotation_row = NULL, show_rownames=T,
  191. clustering_distance_cols="correlation", cluster_cols = F, cluster_rows = T,fontsize_row=6, fontsize_col=8, width = 8,height = 10, main = "1 Month Disease Genes (2900) - Row + log10 Scaled Norm Avg Expression - All Health")
  193. rescued_both <- read.delim("/mnt/humble_50t/alex/harini_rnaseq_jan2023/harini_1_2_overlap_rescueGenes.txt",header=F)
  194. norm.counts.overlaprescue <- avg_1mo_disease[(rownames(avg_1mo_disease) %in% rescued_both$V1),]
  195. pheatmap(as.matrix(log10(1+norm.counts.overlaprescue)), scale="row", color = cols, annotation_col = NULL, annotation_row = NULL, show_rownames=T,
  196. clustering_distance_cols="correlation", cluster_cols = F, cluster_rows = T,fontsize_row=6, fontsize_col=8, width = 8,height = 10, main = "1 Month Expression 1+2 Month Overlap Disease Genes (790)\n Row + log10 Scaled Norm Avg Expression - All Health")
  198. avg_line_1mo <- as.data.frame(melt(as.matrix(1+log10(avg_1mo_disease))))
  199. #order by WT expression
  200. avg_line_1mo$Var1 <- factor(avg_line_1mo$Var1, levels = c(rownames(avg_1mo_disease[order(avg_1mo_disease$WT),])))
  201. ggplot(data = avg_line_1mo) +
  202. geom_point(aes(x = Var1, y = value, colour = Var2, group = Var2,alpha=.5))+
  203. scale_colour_manual(values = c("black", "darkgreen", "firebrick1")) +
  204. labs(title = "1 Month Disease Genes (2900) - log10 Normalized Avg Expression - All Health",
  205. x= "Gene - Sorted on WT Expression", y = "log10 Normalized Avg Expression" ,color="Genotype") +
  206. theme(axis.title.x = element_text(face="bold", size=f),axis.title.y = element_text(face="bold", size=f),
  207. axis.text.x = element_blank(),axis.text.y = element_text(face="bold", size=f), title = element_text(face="bold", size=f),
  208. legend.title = element_text(face="bold", size=f-3),legend.text = element_text(face="bold", size=f-4))+
  209. guides(color = guide_legend(override.aes = list(size=5)), alpha = "none")
  212. #2 months timepoint
  213. norm.counts_2mo <- norm.counts[(sample.info$Timepoint == "2_month")]
  214. #build an average per condition df
  215. avg_2mo <- data.frame(
  216. row.names = row.names(norm.counts_2mo),
  217. WT = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control"),]$Sample_ID)]),
  218. E2KO = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO"),]$Sample_ID)]),
  219. G118E = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E"),]$Sample_ID)]))
  221. #length(which((avg_2mo$E2KO > avg_2mo$WT) & (avg_2mo$E2KO < avg_2mo$G118E) | ((avg_2mo$E2KO < avg_2mo$WT) & (avg_2mo$E2KO > avg_2mo$G118E))))
  222. #filter! to disease genes
  223. dim(G118EvsWT_2mo_degs)[1]
  224. avg_2mo_disease <- avg_2mo[(row.names(avg_2mo) %in% G118EvsWT_2mo_degs$Gene_ID),]
  225. length(which((avg_2mo_disease$E2KO < avg_2mo_disease$WT) & (avg_2mo_disease$E2KO > avg_2mo_disease$G118E)))
  226. length(which((avg_2mo_disease$E2KO > avg_2mo_disease$WT) & (avg_2mo_disease$E2KO < avg_2mo_disease$G118E)))
  227. (length(which((avg_2mo_disease$E2KO < avg_2mo_disease$WT) & (avg_2mo_disease$E2KO > avg_2mo_disease$G118E))) + length(which((avg_2mo_disease$E2KO > avg_2mo_disease$WT) & (avg_2mo_disease$E2KO < avg_2mo_disease$G118E)))) / dim(G118EvsWT_2mo_degs)[1]
  229. rescue_2mo <- avg_2mo_disease[((avg_2mo_disease$E2KO < avg_2mo_disease$WT) & (avg_2mo_disease$E2KO > avg_2mo_disease$G118E)) |
  230. ((avg_2mo_disease$E2KO > avg_2mo_disease$WT) & (avg_2mo_disease$E2KO < avg_2mo_disease$G118E)),]
  232. rescue_2mo$pct_rescue <- 100*abs((rescue_2mo$G118E - rescue_2mo$E2KO)/(rescue_2mo$G118E - rescue_2mo$WT))
  233. pa2<- ggplot(rescue_2mo, aes(pct_rescue)) +
  234. geom_histogram(aes(y = after_stat(count / sum(count))),color = "#000000", fill = "#0099F8") +
  235. scale_y_continuous(labels = scales::percent)+
  236. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  237. theme_bw()+
  238. theme(text = element_text(size = f, face = "bold"))
  239. ggsave("2_month_rescuegenes_pct.pdf",plot = pa2, device = "pdf", width = 4, height = 2.5, units = c("in"),dpi = 300, scale = 2)
  241. pd1<-ggplot((rescue_2mo[order(rescue_2mo$pct_rescue),]), aes(y=pct_rescue,x=rownames(rescue_2mo))) +
  242. geom_point(aes(color=pct_rescue))+
  243. scale_color_gradient(low = "gray40", high="purple")+
  244. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",
  245. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Genes", y = "Percent 'Rescue'",color="Percent\nRescue") +
  246. theme_classic()+
  247. geom_hline(yintercept = 50)+
  248. theme(text = element_text(size = f, face = "bold"),axis.text.x = element_blank())
  249. ggsave("2_month_rescuegenes_pct_manhattan.pdf",plot = pd1, device = "pdf", width = 180, height = 85, units = c("mm"),dpi = 300, scale = 1.5)
  251. rescue_2mo$dir <- ifelse(rescue_2mo$WT > rescue_2mo$G118E,"down","up")
  252. pd2<-ggplot() +
  253. geom_histogram(data=rescue_2mo[(rescue_2mo$dir=="up"),],color = "#000000", fill = "red", aes(x = pct_rescue,y = after_stat(count / sum(count))))+
  254. geom_histogram(data=rescue_2mo[(rescue_2mo$dir=="down"),],color = "#000000", fill = "blue", aes(x = pct_rescue,y = -after_stat(count / sum(count))))+
  255. theme_bw()+
  256. geom_vline(xintercept = 50,linetype="dotdash")+
  257. scale_y_continuous(limits=c(-0.075,0.075),labels = c("8%","4%","0%","4%","8%"))+
  258. labs(title = "Direction and Magnitude of Rescue", x = "Percent of Rescue", y = "Density",fill="Direction",
  259. subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",caption = "abs(G118E-E2KO)/abs(G118E-WT)") +
  260. coord_flip()+
  261. theme(text = element_text(size = f, face = "bold"),plot.subtitle = element_text(size = f-2, face = "bold"))
  262. ggsave("2_month_rescuegenes_pct_pyramid.pdf",plot = pd2, device = "pdf", width = 90, height = 90, units = c("mm"),dpi = 300, scale = 1.8)
  264. pd3<-ggplot(rescue_2mo, aes(x=pct_rescue,y=1)) +
  265. geom_boxplot(alpha=.9,color="purple",lwd=1.5)+
  266. geom_jitter(height = .5,alpha=.7,size=2)+
  267. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",
  268. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Genes") +
  269. theme_classic()+
  270. geom_vline(xintercept = 50,linetype="solid",linewidth=1.1,color="gray45")+
  271. theme(text = element_text(size = f+2, face = "bold"),axis.text.y = element_blank())
  272. ggsave("2_month_rescuegenes_pct_dotbox.pdf",plot = pd3, device = "pdf", width = 180, height = 85, units = c("mm"),dpi = 300, scale = 1.4)
  274. rescue_2mo$bin <- cut(rescue_2mo$pct_rescue, breaks=c(0,25,50,75,100), labels=c("1-25","25-50","50-75","75-100"))
  275. pd5<-ggplot(rescue_2mo, aes(x=pct_rescue,y=bin)) +
  276. geom_boxplot(alpha=.7,color="black",lwd=1.3)+
  277. geom_jitter(height = .5,alpha=.6,size=1.7,shape=1)+
  278. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",
  279. caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Bins") +
  280. theme_classic()+
  281. geom_vline(xintercept = 50,linetype="solid",linewidth=1.1,color="gray45")+
  282. theme(text = element_text(size = f+2, face = "bold"),axis.text.y = element_blank())
  283. ggsave("2_month_rescuegenes_pct_dotbin.pdf",plot = pd5, device = "pdf", width = 180, height = 85, units = c("mm"),dpi = 300, scale = 1.8)
  285. pd6<-ggplot(rescue_2mo, aes(pct_rescue)) +
  286. geom_histogram(bins=4,breaks=c(0,25,50,75,100),
  287. aes(y = after_stat(count / sum(count))),color = "#000000", fill = "#0099F8") +
  288. scale_y_continuous(labels = scales::percent)+
  289. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Percent of Genes") +
  290. theme_bw()+
  291. theme(text = element_text(size = f, face = "bold"))+
  292. coord_flip()
  293. ggsave("2_month_rescuegenes_pct2.pdf",plot = pd6, device = "pdf", width = 180, height = 85, units = c("mm"),dpi = 300, scale = 1.8)
  295. pd7<-ggplot(rescue_2mo, aes(pct_rescue)) +
  296. geom_histogram(bins=4,breaks=c(0,25,50,75,100),color = "#000000", fill = "#0099F8") +
  297. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  298. theme_bw()+
  299. theme(text = element_text(size = f, face = "bold"))+
  300. coord_flip()
  301. ggsave("2_month_rescuegenes_pct3.pdf",plot = pd7, device = "pdf", width = 180, height = 85, units = c("mm"),dpi = 300, scale = 1.8)
  303. # 3 pdf
  305. length(which(rescue_2mo$pct_rescue < 25))
  306. length(which(rescue_2mo$pct_rescue > 25 & rescue_2mo$pct_rescue < 50))
  307. length(which(rescue_2mo$pct_rescue > 50 & rescue_2mo$pct_rescue < 75))
  308. length(which(rescue_2mo$pct_rescue > 75 & rescue_2mo$pct_rescue < 100))
  310. #check health splits
  312. #healthy
  313. avg_2mo_healthy <- data.frame(
  314. row.names = row.names(norm.counts_2mo),
  315. WT = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]),
  316. E2KO = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]),
  317. G118E = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E" & sample.info$RelativeNeuronHealth == "Higher"),]$Sample_ID)]))
  319. #length(which((avg_2mo$E2KO > avg_2mo$WT) & (avg_2mo$E2KO < avg_2mo$G118E) | ((avg_2mo$E2KO < avg_2mo$WT) & (avg_2mo$E2KO > avg_2mo$G118E))))
  320. #filter! to disease genes
  321. dim(G118EvsWT_2mo_degs)[1]
  322. avg_2mo_healthy_disease <- avg_2mo_healthy[(row.names(avg_2mo_healthy) %in% G118EvsWT_2mo_degs$Gene_ID),]
  323. length(which((avg_2mo_healthy_disease$E2KO < avg_2mo_healthy_disease$WT) & (avg_2mo_healthy_disease$E2KO > avg_2mo_healthy_disease$G118E)))
  324. length(which((avg_2mo_healthy_disease$E2KO > avg_2mo_healthy_disease$WT) & (avg_2mo_healthy_disease$E2KO < avg_2mo_healthy_disease$G118E)))
  325. (length(which((avg_2mo_healthy_disease$E2KO < avg_2mo_healthy_disease$WT) & (avg_2mo_healthy_disease$E2KO > avg_2mo_healthy_disease$G118E))) + length(which((avg_2mo_healthy_disease$E2KO > avg_2mo_healthy_disease$WT) & (avg_2mo_healthy_disease$E2KO < avg_2mo_healthy_disease$G118E)))) / dim(G118EvsWT_2mo_degs)[1]
  327. #unhealthy
  328. avg_2mo_unhealthy <- data.frame(
  329. row.names = row.names(norm.counts_2mo),
  330. WT = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]),
  331. E2KO = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]),
  332. G118E = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E" & sample.info$RelativeNeuronHealth == "Lower"),]$Sample_ID)]))
  334. #length(which((avg_2mo$E2KO > avg_2mo$WT) & (avg_2mo$E2KO < avg_2mo$G118E) | ((avg_2mo$E2KO < avg_2mo$WT) & (avg_2mo$E2KO > avg_2mo$G118E))))
  335. #filter! to disease genes
  336. dim(G118EvsWT_2mo_degs)[1]
  337. avg_2mo_unhealthy_disease <- avg_2mo_unhealthy[(row.names(avg_2mo_unhealthy) %in% G118EvsWT_2mo_degs$Gene_ID),]
  338. length(which((avg_2mo_unhealthy_disease$E2KO < avg_2mo_unhealthy_disease$WT) & (avg_2mo_unhealthy_disease$E2KO > avg_2mo_unhealthy_disease$G118E)))
  339. length(which((avg_2mo_unhealthy_disease$E2KO > avg_2mo_unhealthy_disease$WT) & (avg_2mo_unhealthy_disease$E2KO < avg_2mo_unhealthy_disease$G118E)))
  340. (length(which((avg_2mo_unhealthy_disease$E2KO < avg_2mo_unhealthy_disease$WT) & (avg_2mo_unhealthy_disease$E2KO > avg_2mo_unhealthy_disease$G118E))) + length(which((avg_2mo_unhealthy_disease$E2KO > avg_2mo_unhealthy_disease$WT) & (avg_2mo_unhealthy_disease$E2KO < avg_2mo_unhealthy_disease$G118E)))) / dim(G118EvsWT_2mo_degs)[1]
  342. #get gene symbols
  343. namedf <- G118EvsWT_2mo[,c(1,2)]
  344. names(namedf) <- c("Row.names","Gene_symbol")
  345. avg_2mo_disease$Row.names <- rownames(avg_2mo_disease)
  346. avg_2mo_disease <- merge(avg_2mo_disease,namedf,by="Row.names",all.x=T)
  347. rownames(avg_2mo_disease) <- avg_2mo_disease$Gene_symbol
  348. avg_2mo_disease$Row.names <- NULL
  349. avg_2mo_disease$Gene_symbol <- NULL
  350. #write out
  351. write.table(rownames(avg_2mo_disease[((avg_2mo_disease$E2KO < avg_2mo_disease$WT) & (avg_2mo_disease$E2KO > avg_2mo_disease$G118E)) |
  352. ((avg_2mo_disease$E2KO > avg_2mo_disease$WT) & (avg_2mo_disease$E2KO < avg_2mo_disease$G118E)),]),"harini_2month_rescueGenes.txt" ,sep='\t', row.names=F, quote=F, col.names = F)
  354. #maybe try rank, idk
  355. #rankedbigavg_logcpm <- as.data.frame(t(apply(bigavg_logcpm, 1, rank)))
  357. cols <- colorRampPalette(c("white", "firebrick1", "firebrick4"))(30)
  358. pheatmap(as.matrix(log10(1+avg_2mo_disease)), scale="row", color = cols, annotation_col = NULL, annotation_row = NULL, show_rownames=T,
  359. clustering_distance_cols="correlation", cluster_cols = F, cluster_rows = T,fontsize_row=6, fontsize_col=8, width = 8,height = 10, main = "2 Month Disease Genes (3423) - Row + log10 Scaled Norm Avg Expression - All Health")
  361. rescued_both <- read.delim("/mnt/humble_50t/alex/harini_rnaseq_jan2023/harini_1_2_overlap_rescueGenes.txt",header=F)
  362. norm.counts.overlaprescue <- avg_2mo_disease[(rownames(avg_2mo_disease) %in% rescued_both$V1),]
  363. pheatmap(as.matrix(log10(1+norm.counts.overlaprescue)), scale="row", color = cols, annotation_col = NULL, annotation_row = NULL, show_rownames=T,
  364. clustering_distance_cols="correlation", cluster_cols = F, cluster_rows = T,fontsize_row=6, fontsize_col=8, width = 8,height = 10, main = "2 Month Expression 1+2 Month Overlap Disease Genes (790)\n Row + log10 Scaled Norm Avg Expression - All Health")
  366. avg_line_2mo <- as.data.frame(melt(as.matrix(1+log10(avg_2mo_disease))))
  367. #order by WT expression
  368. avg_line_2mo$Var1 <- factor(avg_line_2mo$Var1, levels = c(rownames(avg_2mo_disease[order(avg_2mo_disease$WT),])))
  369. ps<- ggplot(data = avg_line_2mo) +
  370. geom_point(aes(x = Var1, y = value, colour = Var2, group = Var2,alpha=.4),size=.4)+
  371. scale_colour_manual(values = c("black", "darkgreen", "firebrick1")) +
  372. labs(title = "2 Month Disease Genes (3423)\nLog10 Normalized Avg Expression - All Health\nOne Point of each color per Gene",
  373. x= "Gene - Sorted on WT Expression", y = "log10 Normalized Avg Expression" ,color="Genotype") +
  374. theme(axis.title.x = element_text(face="bold", size=f-2),axis.title.y = element_text(face="bold", size=f),
  375. axis.text.x = element_blank(),axis.text.y = element_text(face="bold", size=f), title = element_text(face="bold", size=f),
  376. legend.title = element_text(face="bold", size=f-4),legend.text = element_text(face="bold", size=f-5))+
  377. guides(color = guide_legend(override.aes = list(size=4)), alpha = "none")
  378. ggsave("2_month_diseasegenes_log10avgnormExpresssion.pdf",plot = ps, device = "pdf", width = 3, height = 2.5, units = c("in"),dpi = 300, scale = 2)
  381. hp1<-ggplot(data = avg_line_2mo) +
  382. geom_point(aes(x = Var1, y = value, colour = Var2, group = Var2,alpha=.4),size=.4)+
  383. scale_colour_manual(values = c("black", "skyblue1", "maroon4")) +
  384. labs(title = "2 Month Disease Genes (3423)\nLog10 Normalized Avg Expression - All Health\nOne Point of each color per Gene",
  385. x= "Gene - Sorted on WT Expression", y = "log10 Normalized Avg Expression" ,color="Genotype") +
  386. theme(axis.title.x = element_text(face="bold", size=f-2),axis.title.y = element_text(face="bold", size=f),
  387. axis.text.x = element_blank(),axis.text.y = element_text(face="bold", size=f), title = element_text(face="bold", size=f),
  388. legend.title = element_text(face="bold", size=f-4),legend.text = element_text(face="bold", size=f-5))+
  389. guides(color = guide_legend(override.aes = list(size=4)), alpha = "none")
  392. #2 month
  393. norm.counts_2mo <- norm.counts[(sample.info$Timepoint == "2_month")]
  394. #build an average per condition df
  395. avg_2mo <- data.frame(
  396. row.names = row.names(norm.counts_2mo),
  397. WT = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "Isogenic_WT_Control"),]$Sample_ID)]),
  398. E2KO = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E-E2KO"),]$Sample_ID)]),
  399. G118E = rowMeans(norm.counts_2mo[,(names(norm.counts_2mo) %in% sample.info[(sample.info$Genotype == "G118E"),]$Sample_ID)]))
  401. #length(which((avg_2mo$E2KO > avg_2mo$WT) & (avg_2mo$E2KO < avg_2mo$G118E) | ((avg_2mo$E2KO < avg_2mo$WT) & (avg_2mo$E2KO > avg_2mo$G118E))))
  402. #filter! to disease genes
  403. dim(G118EvsWT_2mo_degs)[1]
  404. avg_2mo_disease <- avg_2mo[(row.names(avg_2mo) %in% G118EvsWT_2mo_degs$Gene_ID),]
  405. rescue_2mo <- avg_2mo_disease[((avg_2mo_disease$E2KO < avg_2mo_disease$WT) & (avg_2mo_disease$E2KO > avg_2mo_disease$G118E)) |
  406. ((avg_2mo_disease$E2KO > avg_2mo_disease$WT) & (avg_2mo_disease$E2KO < avg_2mo_disease$G118E)),]
  407. rescue_2mo$pct_rescue <- 100*abs((rescue_2mo$G118E - rescue_2mo$E2KO)/(rescue_2mo$G118E - rescue_2mo$WT))
  408. ggplot(rescue_2mo, aes(pct_rescue)) +
  409. geom_histogram(aes(y = after_stat(count / sum(count))),color = "#000000", fill = "#0099F8") +
  410. scale_y_continuous(labels = scales::percent)+
  411. labs(title = "What is the Magnitude of Expression 'Rescue'?",subtitle = "2 Month Disease Signature Genes with Expression Rescue (2226/3423)",caption = "abs(G118E-E2KO)/abs(G118E-WT)",x = "Percent 'Rescue'", y = "Count") +
  412. theme_bw()+
  413. theme(text = element_text(size = f, face = "bold"))
  415. #format table
  416. rescue_2mo$direction <- ifelse(rescue_2mo$WT > rescue_2mo$G118E,"down","up")
  417. names <- G118EvsWT_2mo_degs[,c(1,2)]
  418. rownames(names) <- names$Gene_ID
  419. names$Gene_ID <- NULL
  420. rescue_2mo <- merge(rescue_2mo,names,by='row.names',all.x=T)
  421. names(rescue_2mo)[1] <- "Gene_ID"
  422. write.table(rescue_2mo,file="harini_2month_rescueGenes.txt",quote = F,row.names = F)

G118E_NGN2_downstream_E2KO.R at commit 0b1d78e, under MIT · at the source

Overview

Authors: Harini P Tirumala1,2, Li Wang1,2,3, Yan Li1,2, Sameer S Bajikar1,2,4, Ashley G Anderson1,2, Wei Wang1,2, Alexander J Trostle2,5,6, Mahla Zahabiyon1,2, Aleksandar Bajic1,2,7, Jean J Kim8, Hu Chen2,5, Zhandong Liu2,5, Huda Y Zoghbi1,2,9,5
ORCID iDs: Harini P Tirumala, Li Wang, Yan Li, Sameer S Bajikar, Ashley G Anderson, Wei Wang, Alexander J Trostle, Aleksandar Bajic, Jean J Kim, Hu Chen, Zhandong Liu, Huda Y Zoghbi
  1. Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030
  2. Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030
  3. Present address: Department of Biology, Stanford University, Stanford, CA 94305
  4. Present address: Departments of Cell Biology and Biomedical Engineering, University of Virginia, Charlottesville, VA 22903
  5. Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030
  6. Present address: Department of Surgery, UT Southwestern Medical Center, Dallas, TX 75390
  7. Advanced Technology Cores, Baylor College of Medicine, Houston, Texas 77030
  8. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030
  9. Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas 77030
Institutions: Baylor College of Medicine (United States); Texas Children's Hospital (United States); Howard Hughes Medical Institute (United States)
Journal: Science translational medicine, volume 18, issue 839, article eadq4529
Dates: published online 4 March 2026; in print 4 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/scitranslmed.adq4529 · PMID 41779872 · PMCID PMC13061089 · OpenAlex W7133563503
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population)
MeSH: Alternative Splicing*, Methyl-CpG-Binding Protein 2*, Rett Syndrome*, Animals, Exons, Humans, Induced Pluripotent Stem Cells, Mice, Mutation, Neurons, Protein Isoforms, RNA, Messenger (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH HHS (S10 OD028591); Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103555); NCI NIH HHS (P30 CA125123); FUNDING; National Institute of Neurological Disorders and Stroke (5R01NS057819, F32NS122920); NINDS NIH HHS (R01 NS057819, F32 NS122920); NICHD NIH HHS (P50 HD103555); National Cancer Institute (NIH P30 CA125123); Howard Hughes Medical Institute; The Henry Engel Fund; NIH Office of the Director (NIH S10OD028591)
Citations: cited by 3 papers (Europe PMC); 64 references in the paper

Abstract

Rett syndrome (RTT) is a neurological disorder caused by loss-of-function mutations in methyl CpG binding protein 2 (MECP2), a transcriptional regulator essential for maintenance of normal neuronal function. The current FDA-approved treatment for RTT, Trofinetide, mildly alleviates some symptoms. In contrast, re-introducing MeCP2 or increasing its amount through transgenesis in mouse RTT models improves most neurological phenotypes and enhances survival. Here, we devised a therapeutic strategy to moderately increase MeCP2 protein by modulating the alternative splicing of MECP2 to switch the less efficiently translated e2 to the more efficiently translated e1 isoform. We deleted Mecp2 exon 2 (unique to e2), leading to production of only e1 mRNA, and show this upregulates MeCP2 by 50-60% in mice. Next, we investigated the consequences of isoform switching in two independent RTT induced pluripotent stem cell (iPSC)-derived neuron models harboring mutations that reduce both MeCP2 expression and function. Exon 2 deletion in MeCP2-G118E patient-derived neurons upregulated MeCP2, ameliorated morphological and electrophysiological changes and corrected the dysregulated transcriptome in these neurons. Isoform switching in MeCP2-T158M patient-derived neurons, modelling a severe RTT mutation, only modestly affected MeCP2 protein abundance and despite this, led to a partial transcriptomic rescue. Lastly, an exon 2-skipping Morpholino upregulated MeCP2-E1 in vivo in mice. These data set the stage for a potential therapeutic strategy using antisense oligonucleotides to promote isoform switching in patients with RTT who carry partially functioning alleles of MECP2.

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

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Zenodo 18140370

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
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Found in: “DATA, CODE AND MATERIALS AVAILABILITY”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), data.table (3 files), limma (3 files), pheatmap (3 files), tidyverse (3 files), DESeq2 (2 files), reshape2 (2 files), WGCNA (2 files)
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yanl54/mecp2_e2ko_rnaseq

License: MIT
State: the link answers, verified on 30 September 2026
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Commit: 0b1d78ee1d339a669f624b380b0bb8663eb8edf6, 17 November 2025
Languages: R (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), data.table (3 files), limma (3 files), pheatmap (3 files), tidyverse (3 files), DESeq2 (2 files), reshape2 (2 files), WGCNA (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/YanL54/MECP2_E2KO_RNAseq/tree/v1.0.1

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

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Data

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Data, code and materials availability

All data associated with this study are present in the paper or supplementary materials. All materials newly generated in this study (E2KO mouse line, two G118E-E2KO iPSC clones and one G118E iPSC clone) will be available upon request from the corresponding author under a Material Transfer Agreement with Baylor College of Medicine. The T158M-MU-E2KO iPSC line which will be deposited at RSRT. The T158M-WT and MU iPSCs used in this study were provided by RSRT under an MTA. All other materials used or generated in this study are commercially available or will be supplied upon reasonable request. The data presented in this study are deposited on GEO (GSE268177). RNA-sequencing analysis code is available on Zenodo (DOI: 10.5281/zenodo.18140370).

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Tirumala, H. P., Wang, L., Li, Y., Bajikar, S. S., Anderson, A. G., Wang, W., Trostle, A. J., Zahabiyon, M., Bajic, A., Kim, J. J., Chen, H., Liu, Z., & Zoghbi, H. Y. (2026). Modulating alternative splicing of &lt;i&gt;MECP2&lt;/i&gt; is a potential therapeutic strategy for Rett syndrome. Science translational medicine, 18(839), eadq4529. https://doi.org/10.1126/scitranslmed.adq4529

BibTeX

@article{tirumala2026modulating,
author = {Tirumala, Harini P and Wang, Li and Li, Yan and Bajikar, Sameer S and Anderson, Ashley G and Wang, Wei and Trostle, Alexander J and Zahabiyon, Mahla and Bajic, Aleksandar and Kim, Jean J and Chen, Hu and Liu, Zhandong and Zoghbi, Huda Y},
title = {{Modulating alternative splicing of \&lt;i\&gt;MECP2\&lt;/i\&gt; is a potential therapeutic strategy for Rett syndrome}},
journal = {Science translational medicine},
year = {2026},
month = mar,
volume = {18},
number = {839},
pages = {eadq4529},
publisher = {American Association for the Advancement of Science},
issn = {1946-6234},
doi = {10.1126/scitranslmed.adq4529},
url = {https://doi.org/10.1126/scitranslmed.adq4529},
pmid = {41779872},
pmcid = {PMC13061089}
}

RIS

TY - JOUR
AU - Tirumala, Harini P
AU - Wang, Li
AU - Li, Yan
AU - Bajikar, Sameer S
AU - Anderson, Ashley G
AU - Wang, Wei
AU - Trostle, Alexander J
AU - Zahabiyon, Mahla
AU - Bajic, Aleksandar
AU - Kim, Jean J
AU - Chen, Hu
AU - Liu, Zhandong
AU - Zoghbi, Huda Y
TI - Modulating alternative splicing of &lt;i&gt;MECP2&lt;/i&gt; is a potential therapeutic strategy for Rett syndrome
T2 - Science translational medicine
J2 - Sci Transl Med
PY - 2026
DA - 2026/03/04
VL - 18
IS - 839
SP - eadq4529
SN - 1946-6234
PB - American Association for the Advancement of Science
DO - 10.1126/scitranslmed.adq4529
UR - https://doi.org/10.1126/scitranslmed.adq4529
LA - en
ER -

CSL-JSON

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"id": "10.1126/scitranslmed.adq4529",
"type": "article-journal",
"title": "Modulating alternative splicing of &lt;i&gt;MECP2&lt;/i&gt; is a potential therapeutic strategy for Rett syndrome",
"container-title": "Science translational medicine",
"author": [
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"container-title-short": "Sci Transl Med",
"volume": "18",
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"page": "eadq4529",
"DOI": "10.1126/scitranslmed.adq4529",
"PMID": "41779872",
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"ISSN": "1946-6234",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/scitranslmed.adq4529",
"language": "en",
"issued": {
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