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Suppression of astrocyte BMP signaling improves molecular signatures and functional deficits in a fragile X syndrome mouse model.

Code ↔ Paper

7 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.

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  1. [1] § Methods › Method details › ER-TurboID proteomics › Proteomic analysis ↔ Proteomic_Analysis_Dengetal.Rmd, lines 324–459 · score 0.71 · TMT signal, l2fc, RRHO, hypergeometric, fold change, contaminants
  2. [2] § Methods › Method details › Glyoxal-fixed astrocyte nuclei transcriptomics › nRNAseq analysis ↔ RNAseq_Analysis_Dengetal.Rmd, lines 594–663 · score 0.63 · mouse MSigDB, fold change, Wikipathways, BioCarta, Reactome, ranking
  3. [3] § Methods › Method details › ER-TurboID proteomics › Proteomic analysis ↔ Proteomic_Analysis_Dengetal.Rmd, lines 194–256 · score 0.63 · Cellular compartment, smFP, Cytoscape, ontology, enriched, log10
  4. [4] § Results › Fmr1 KO astrocytes have a hypermetabolic transcriptional state in vivo ↔ RNAseq_Analysis_Dengetal.Rmd, lines 390–464 · score 0.63 · mRNA, FMRP targets, log2 fold change, enriched, astrocyte, FXS
  5. [5] § Results › Fmr1 KO astrocytes have a hypermetabolic transcriptional state in vivo ↔ RNAseq_Analysis_Dengetal.Rmd, lines 390–464 · score 0.62 · mRNA, FMRP targets, log2 fold change, Lab, Density, enriched
  6. [6] § Results › Fmr1 KO astrocytes downregulate extracellular matrix proteins and secretory machinery ↔ Proteomic_Analysis_Dengetal.Rmd, lines 324–459 · score 0.56 · TMT signal, log2 fold change, correlation, RNA, proteomics, DEGs
  7. [7] § Results › Interrogating in vivo astrocyte endoplasmic reticulum-passaged proteins ↔ Proteomic_Analysis_Dengetal.Rmd, lines 33–70 · score 0.51 · mass spectrometry, smFP, streptavidin, Turbo, TMT, FXS

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The authors' code

R Markdown · 1,188 lines · 52 KB · CC-BY-4.0 · 4 matches

  1. ---
  2. title: "Proteomic Analyses"
  3. output:
  4. pdf_document: default
  5. html_notebook: default
  6. html_document:
  7. df_print: paged
  8. ---
  10. ```{r setup, include=FALSE}
  11. knitr::opts_chunk$set(warning = FALSE, message = FALSE)
  12. ```
  15. Proteomic Analyses for Deng et al. 2024
  17. This R notebook contains the code used for creating the figures and tables related to proteomic data in Deng et al., 2024. The code uses output from mass spectrometry (Census, see Methods section) and can be run in RStudio. The code was tested with Windows 11 Pro; no non-standard hardware is required.
  19. Packages can be installed as below (for "example_package"). Estimated time for installing all packages and running all code on typical desktop computer: 2-4 hrs. Instructions are included as comments on relevant lines of code, and quantitative results that are used in the figures and listed in supplementary data tables can be reproduced by following the respective sections.
  22. ```{r}
  24. #install.packages("example_package")
  25. #OR
  26. #if (!require("BiocManager", quietly = TRUE))
  27. # install.packages("BiocManager")
  28. #BiocManager::install("example_package")
  30. ```
  33. Create Supplementary Table 6: TMT raw values
  34. ```{r}
  36. library(dplyr)
  37. library(tibble)
  38. library(writexl)
  39. library(ggplot2)
  40. library(ggrepel)
  42. data_genename <- read.csv("NAJD-1-16_v2_jd.csv") #output from mass spectrometry (Census)
  43. data_genename[,27] <- gsub(".*GN\\=", "", data_genename[,27])
  44. data_genename[,27] <- gsub(" PE.*", "", data_genename[,27])
  46. data_genename <- distinct(data_genename,DESCRIPTION, .keep_all=TRUE) #removes 22 out of 2177
  48. # preparing final version: no contaminants, streptavidin
  49. data_genename_final <- data_genename[!grepl("contaminant",data_genename$LOCUS),] #from 2155 to 2139
  50. data_genename_final <- data_genename_final[!grepl("Streptavidin",data_genename_final$DESCRIPTION),] #from 2139 to 2138
  51. data_genename_final <- data_genename_final[,c(1,27,65,28:47)]
  53. colnames(data_genename_final) <- c("Locus","Gene","Description",
  54. "WT1","WT2","WT3",
  55. "WT;Smad4cKO1","WT;Smad4cKO2","WT;Smad4cKO3",
  56. "FXS1","FXS2","FXS3",
  57. "FXS;Smad4cKO1","FXS;Smad4cKO2","FXS;Smad4cKO3",
  58. "WT smFP","WT;Smad4cKO smFP","FXS smFP","FXS;Smad4cKO smFP",
  59. "WT Avg","WT;Smad4cKO Avg","FXS Avg","FXS;Smad4cKO Avg"
  60. )
  61. data_genename_final <- data_genename_final[order(data_genename_final$Gene),]
  63. data_genename_final$AvgTurbo <- rowMeans(data_genename_final[,c(4:15)])
  64. data_genename_final$AvgsmFP <- rowMeans(data_genename_final[,c(16:19)])
  65. data_genename_final$Turbo_over_smFP <- data_genename_final$AvgTurbo/data_genename_final$AvgsmFP
  67. write_xlsx(data_genename_final, path = "Supplementary Table 6.xlsx")
  70. ```
  73. Create figure S8G (cell type verification)
  74. ```{r}
  75. # Heatmaps of cell type
  76. library(pheatmap)
  77. library(viridis)
  78. library(ggplot2)
  80. cell_type <- read.csv("Boisvert_cell type genes.csv")
  82. cell_type_astro <- data_genename[data_genename$DESCRIPTION %in% cell_type$Astrocyte,]
  83. cell_type_astro$type <- "astro"
  84. cell_type_endo <- data_genename[data_genename$DESCRIPTION %in% cell_type$Endo,]
  85. cell_type_endo$type <- "endo"
  86. cell_type_microglia <- data_genename[data_genename$DESCRIPTION %in% cell_type$Microglia,]
  87. cell_type_microglia$type <- "microglia"
  88. cell_type_neuron <- data_genename[data_genename$DESCRIPTION %in% cell_type$Neuron,]
  89. cell_type_neuron$type <- "neuron"
  90. cell_type_opc <- data_genename[data_genename$DESCRIPTION %in% cell_type$OPC,]
  91. cell_type_oligo <- data_genename[data_genename$DESCRIPTION %in% cell_type$Oligo,]
  92. cell_type_oligo$type <- "oligo"
  94. cell_type_combined <- rbind(cell_type_astro,cell_type_neuron,cell_type_oligo,cell_type_endo,cell_type_microglia,cell_type_opc)
  95. rownames(cell_type_combined) <- cell_type_combined[,27]
  96. cell_type_combined <- cell_type_combined[!(rownames(cell_type_combined) %in% c("Slc39a10","")),]
  98. cell_type_combined$"log2(Turbo/smFP)" <- log2(as.numeric(cell_type_combined[,51]))
  100. cell_type_average = data.frame(0,0,0,0,0)
  101. colnames(cell_type_average) <- c("Astrocyte","Neuron","Oligodendrocyte","Endothelial","Microglia")
  102. cell_type_average$Astrocyte <- mean(cell_type_combined[c(1:30), 72])
  103. cell_type_average$Endothelial <- mean(cell_type_combined[c(57:59), 72])
  104. cell_type_average$Microglia <- mean(cell_type_combined[c(60:61), 72])
  105. cell_type_average$Neuron <- mean(cell_type_combined[c(31:51), 72])
  106. cell_type_average$Oligodendrocyte <- mean(cell_type_combined[c(52:56), 72])
  108. #cell_type_combined <- cell_type_combined[-c(8, 10, 24, 28, 29, 34, 38),]
  110. rowInfo <- cell_type_combined[,c("DESCRIPTION", "type")]
  111. row.names(rowInfo) <- rowInfo$DESCRIPTION
  112. rowInfo <- dplyr::select(rowInfo, -c("DESCRIPTION"))
  114. data <- cell_type_combined[,c("DESCRIPTION", "log2(Turbo/smFP)")]
  115. row.names(data) <- data$DESCRIPTION
  116. data<- dplyr::select(data, -c("DESCRIPTION"))
  119. mybreaks <- c(seq(0,5, length =50))
  121. map <- pheatmap((data),
  122. annotation_row = rowInfo,
  123. annotation_col = NA,
  124. annotation_colors = NA,
  125. color = plasma(50),
  126. breaks = mybreaks,
  127. show_rownames = TRUE,
  128. show_colnames = TRUE,
  129. annotation_legend = TRUE,
  130. fontsize_row = 6,
  131. fontsize = 8,
  132. cluster_rows = FALSE,
  133. cluster_cols = FALSE,
  134. cellwidth = 20,
  135. cellheight = 5,
  136. width = 2,
  137. height = 2,
  138. gaps_row = c(30,51,56,59))
  139. ggsave("celltype proteomics.pdf", map, width = 2, height = 6, unit = "in")
  144. ```
  147. Create figure S8F (cell type verification)
  148. ```{r}
  149. library(pheatmap)
  151. rowInfo <- data.frame("type"=c("Astrocyte","Neuron","Oligodendrocyte","Endothelial","Microglia"),
  152. "Turbo_smFP_ratio" = c(cell_type_average$Astrocyte,
  153. cell_type_average$Neuron,
  154. cell_type_average$Oligodendrocyte,
  155. cell_type_average$Endothelial,
  156. cell_type_average$Microglia) )
  157. row.names(rowInfo) <- rowInfo$type
  158. rowInfo <- dplyr::select(rowInfo, -c("Turbo_smFP_ratio"))
  160. data <- data.frame("type"=c("Astrocyte","Neuron","Oligodendrocyte","Endothelial","Microglia"),
  161. "Turbo_smFP_ratio" = c(cell_type_average$Astrocyte,
  162. cell_type_average$Neuron,
  163. cell_type_average$Oligodendrocyte,
  164. cell_type_average$Endothelial,
  165. cell_type_average$Microglia) )
  166. row.names(data) <- data$type
  167. data<- dplyr::select(data, -c("type"))
  170. mybreaks <- c(seq(0,2, length =50))
  172. map <- pheatmap((data),
  173. annotation_row = NA,
  174. annotation_col = NA,
  175. annotation_colors = NA,
  176. color = plasma(50),
  177. breaks = mybreaks,
  178. show_rownames = TRUE,
  179. show_colnames = TRUE,
  180. annotation_legend = TRUE,
  181. fontsize_row = 10,
  182. fontsize = 10,
  183. cluster_rows = FALSE,
  184. cluster_cols = FALSE,
  185. cellwidth = 20,
  186. cellheight = 20,
  187. width = 7,
  188. height = 5,
  189. )
  190. ggsave("celltype_average.pdf", map, width = 2, height = 3, unit = "in")
  191. ```
  194. Create figure 4E (Overrepresentation analysis cellular compartment)
  195. ```{r}
  196. library(clusterProfiler)
  197. library(enrichplot)
  198. library(pathview)
  199. library(ggplot2)
  200. organism = "org.Mm.eg.db"
  201. library(organism, character.only = TRUE)
  204. #processing the cytoscape file (created in excel using top 200 proteins by Turbo/smFP ratio)
  206. cytoscape <- read.csv("cytoscape 200 proteins.csv")
  207. rownames(cytoscape) <- cytoscape[,2]
  209. cytoscape <- cytoscape[rownames(cytoscape)!=c("Q6PIU9"),]
  210. cytoscape <- cytoscape[rownames(cytoscape)!=c("Q8C3W1"),]
  211. cytoscape <- cytoscape[rownames(cytoscape)!=c("Q9D7E4"),]
  212. cytoscape <- cytoscape[c(1:200),]
  217. cytoscape_enrich <- enrichGO(gene = rownames(cytoscape),
  218. OrgDb = organism,
  219. keyType = 'SYMBOL',
  220. readable = T,
  221. ont = "ALL",
  222. pAdjustMethod = 'BH',
  223. pvalueCutoff = 0.05,
  224. qvalueCutoff = 0.10,
  225. minGSSize = 5)
  226. cytoscape_enrich_df <- as.data.frame(cytoscape_enrich)
  227. cytoscape_enrich_df_cc <- cytoscape_enrich_df[cytoscape_enrich_df$ONTOLOGY == "CC",]
  229. write.csv(cytoscape_enrich_df_cc, "./cytoscape_enrich_df_cc.csv")
  231. cytoscape_enrich_df_cc_forplot <- cytoscape_enrich_df_cc[c(1:10),]
  232. cytoscape_enrich_df_cc_forplot$logP <- -log(cytoscape_enrich_df_cc_forplot$pvalue,10)
  233. cytoscape_enrich_df_cc_forplot$Description <- c("ER lumen",
  234. "ER protein-containing complex",
  235. "chaperonin-containing T-complex",
  236. "ER-Golgi intermediate compartment",
  237. "integral component of ERM",
  238. "intrinsic component of ERM",
  239. "synapse-associated ECM",
  240. "chaperone complex",
  241. "ER chaperone complex",
  242. "perineuronal net")
  244. barplot_cc <- ggplot(cytoscape_enrich_df_cc_forplot, aes( x = logP, y = reorder(ID, logP), fill = Count))+
  245. geom_bar(stat = "identity") +
  246. labs(x="-log10(P-value)", y="") +
  247. geom_text(aes(label=Description),
  248. hjust=0, nudge_x = -10, ) +
  249. theme_classic() +
  250. scale_fill_gradient2(low="white",high="red")
  251. barplot_cc
  253. ggsave("barplot_cc.pdf", barplot_cc, height = 6, width = 4, unit = 'in')
  256. ```
  259. Loading transcriptomics data (copied from transcriptomics code)
  260. ```{r}
  261. ##requires loading the DESeq_results RData file
  263. library(dplyr)
  264. library(tibble)
  265. library(writexl)
  267. tpmData <- read.csv("tpm.csv") #TPM output file from Homer
  268. tpmFinal <- tpmData[,-(2:7)] #Remove extraneous annotation information
  269. tpmFinal <- tpmFinal[,c(1,2,14,16,19,21,12,13,15,17,18,20,3,4,7,9,10,5,6,8,11)]
  271. rownames(tpmFinal) <- sub("\\|.*", "", tpmFinal$Annotation)
  273. tpmFinal <- rownames_to_column(tpmFinal)
  275. ## DESeq2 with standard parameters was used to perform pairwise comparisons:
  276. ## (1) WT to FXS [termed "crenegonly"]
  277. ## (2) FXS to FXS;Smad4 cKO [termed "koonly"]
  278. ## (3) WT to WT;Smad4 cKO [termed "wtonly"]
  279. ## (4) WT to FXS;Smad4 cKO [termed "kokowtwt" or "comp5"]
  281. ##load the DESeq_results.RData workspace
  282. load("DESeq_results.Rdata")
  284. res_wtonly_frame <- rownames_to_column(as.data.frame(res_wtonly)) #DESeq output comparing WT to WT;Smad4 cKO
  285. res_wtonly_frame <- res_wtonly_frame[,-c(2,4,5,6)]
  286. tpmFinal <- full_join(tpmFinal, res_wtonly_frame, by='rowname')
  288. res_koonly_frame <- rownames_to_column(as.data.frame(res_koonly)) #DESeq output comparing FXS to FXS;Smad4 cKO
  289. res_koonly_frame <- res_koonly_frame[,-c(2,4,5,6)]
  290. tpmFinal <- full_join(tpmFinal, res_koonly_frame, by='rowname')
  292. res_crenegonly_frame <- rownames_to_column(as.data.frame(res_crenegonly)) #DESeq output comparing WT to FXS
  293. res_crenegonly_frame <- res_crenegonly_frame[,-c(2,4,5,6)]
  294. tpmFinal <- full_join(tpmFinal, res_crenegonly_frame, by='rowname')
  296. colnames(tpmFinal) <- c('Gene','Transcript','Annotation','WT1','WT2','WT3','WT4',
  297. 'WT;Smad4cKO1','WT;Smad4cKO2','WT;Smad4cKO3','WT;Smad4cKO4','WT;Smad4cKO5','WT;Smad4cKO6',
  298. 'FXS1','FXS2','FXS3','FXS4','FXS5',
  299. 'FXS;Smad4cKO1','FXS;Smad4cKO2','FXS;Smad4cKO3','FXS;Smad4cKO4',
  300. 'L2FC WT;Smad4cKO vs. WT', 'padj WT;Smad4cKO vs. WT',
  301. 'L2FC FXS;Smad4cKO vs. FXS', 'padj FXS;Smad4cKO vs. FXS',
  302. 'L2FC FXS vs WT', 'padj FXS vs. WT')
  304. tpmFinal$"WT AvgTPM" <- rowMeans(tpmFinal[4:7])
  305. tpmFinal$"WT;Smad4cKO AvgTPM" <- rowMeans(tpmFinal[8:13])
  306. tpmFinal$"FXS AvgTPM" <- rowMeans(tpmFinal[14:18])
  307. tpmFinal$"FXS;Smad4cKO AvgTPM" <- rowMeans(tpmFinal[19:22])
  309. tpmFinal <- tpmFinal[order(tpmFinal$Gene),]
  310. tpmFinal$all_above_1 <- rowMaxs(as.matrix(tpmFinal[,c(29:32)]))>1
  312. rownames(tpmFinal) <- tpmFinal[,1]
  314. res_crenegonly_up <- as.data.frame(subset(subset(res_crenegonly, log2FoldChange > 0.585), padj<0.05))
  315. res_crenegonly_up <- subset(res_crenegonly_up, tpmFinal[row.names(res_crenegonly_up),29]>1)
  316. res_crenegonly_up <- res_crenegonly_up[order(res_crenegonly_up$padj),]
  318. res_crenegonly_down <- as.data.frame(subset(subset(res_crenegonly, log2FoldChange < -0.585), padj<0.05))
  319. res_crenegonly_down <- subset(res_crenegonly_down, tpmFinal[row.names(res_crenegonly_down),29]>1)
  320. res_crenegonly_down <- res_crenegonly_down[order(res_crenegonly_down$padj),]
  321. ```
  324. Creating figure S9: Correlating in vivo transcriptomics and proteomics
  325. ```{r}
  331. data_genename_working <- read.csv("NAJD-1-16_v2_jd.csv")
  332. data_genename_working[,27] <- gsub(".*GN\\=", "", data_genename_working[,27])
  333. data_genename_working[,27] <- gsub(" PE.*", "", data_genename_working[,27])
  334. data_genename_working <- distinct(data_genename_working,DESCRIPTION, .keep_all=TRUE)
  336. data_genename_working <- data_genename_working[!grepl("contaminant",data_genename_working$LOCUS),]
  337. data_genename_working <- data_genename_working[!grepl("Streptavidin",data_genename_working$DESCRIPTION),]
  338. data_genename_working$AvgTurbo <- rowMeans(data_genename_working[,c(28:39)])
  339. data_genename_working$AvgsmFP <- rowMeans(data_genename_working[,c(40:43)])
  340. data_genename_working$Turbo_over_smFP <- data_genename_working$AvgTurbo/data_genename_working$AvgsmFP
  343. data_5_creneg_l2fc <- data_genename_working[,c(27,58)]
  344. data_5_creneg_l2fc[,2] <- log2(as.numeric(data_5_creneg_l2fc[,2]))
  345. rownames(data_5_creneg_l2fc) <- data_5_creneg_l2fc[,1]
  346. data_5_creneg_l2fc$rna <- res_crenegonly[rownames(data_5_creneg_l2fc), "log2FoldChange"]
  347. data_5_creneg_l2fc <- data_5_creneg_l2fc[,-c(1)]
  348. data_5_creneg_l2fc <- na.omit(data_5_creneg_l2fc)
  349. cor_data_5_creneg_l2fc <- cor(data_5_creneg_l2fc, method = 'pearson', use = "pairwise.complete.obs")
  350. #plot(data_5_creneg_l2fc$Cre...KO.WT.1,data_5_creneg_l2fc$rna)
  353. map <- ggplot(data_5_creneg_l2fc, aes(x=rna,y=Cre...KO.WT.1))+geom_point(size=0.2) +
  354. labs(x = "RNA log2FC", y = "Protein log2FC") +
  355. theme_bw() +
  356. theme(axis.text = element_text(size=12),
  357. axis.title = element_text(size=14),
  358. aspect.ratio=1) +
  359. stat_smooth(method=lm, se = FALSE)
  361. ggsave("l2fc protein rna all proteins.pdf", map, width = 3, height = 3, unit = "in")
  363. #count <- na.omit(data_5_creneg_l2fc)
  367. #only DEGs
  368. data_5_creneg_l2fc_DEGs <- data_5_creneg_l2fc[c(rownames(res_crenegonly_down),rownames(res_crenegonly_up)),]
  369. data_5_creneg_l2fc_DEGs <- na.omit(data_5_creneg_l2fc_DEGs)
  370. cor_data_5_creneg_l2fc_DEGs <- cor(data_5_creneg_l2fc_DEGs, method = 'pearson', use = "pairwise.complete.obs")
  371. #plot(data_5_creneg_l2fc_DEGs$Cre...KO.WT.1,data_5_creneg_l2fc_DEGs$rna)
  373. map <- ggplot(data_5_creneg_l2fc_DEGs, aes(x=rna,y=Cre...KO.WT.1))+geom_point(size=0.2) +
  374. labs(x = "RNA log2FC", y = "Protein log2FC") +
  375. theme_bw() +
  376. theme(axis.text = element_text(size=12),
  377. axis.title = element_text(size=14),
  378. aspect.ratio=1) +
  379. stat_smooth(method=lm, se=FALSE)
  381. ggsave("l2fc protein rna DEGs.pdf", map, width = 3, height = 3, unit = "in")
  383. #only DEPs
  384. rownames(data_genename_working) <- data_genename_working$DESCRIPTION
  386. data_5_creneg_l2fc_DEPs_list <- subset(data_genename_working, data_genename_working[,59]<0.05)
  387. data_5_creneg_l2fc_DEPs_list <- subset(data_5_creneg_l2fc_DEPs_list, data_5_creneg_l2fc_DEPs_list[,58]<0.8 | data_5_creneg_l2fc_DEPs_list[,58]>1.25)
  388. data_5_creneg_l2fc_DEPs <- data_5_creneg_l2fc[rownames(as.data.frame(data_5_creneg_l2fc_DEPs_list)),]
  390. data_5_creneg_l2fc_DEPs <- na.omit(data_5_creneg_l2fc_DEPs)
  391. cor_data_5_creneg_l2fc_DEPs <- cor(data_5_creneg_l2fc_DEPs, method = 'pearson', use = "pairwise.complete.obs")
  392. #plot(data_5_creneg_l2fc_DEPs$Cre...KO.WT.1,data_5_creneg_l2fc_DEPs$rna)
  394. map <- ggplot(data_5_creneg_l2fc_DEPs, aes(x=rna,y=Cre...KO.WT.1))+geom_point(size=0.2) +
  395. labs(x = "RNA log2FC", y = "Protein log2FC") +
  396. theme_bw() +
  397. theme(axis.text = element_text(size=12),
  398. axis.title = element_text(size=14),
  399. aspect.ratio=1) +
  400. stat_smooth(method=lm, se = FALSE)
  402. ggsave("l2fc protein rna DEPs.pdf", map, width = 3, height = 3, unit = "in")
  406. # TMT raw intensity vs. RNA TPM graph
  407. library(ggplot2)
  408. TMT_RNA <- subset(data_genename_working,data_genename_working$Turbo_over_smFP>1.5) #1105 proteins
  409. TMT_RNA <- TMT_RNA[,c(27,44)]
  410. TMT_RNA$TPM <- tpmFinal[rownames(TMT_RNA),29]
  411. colnames(TMT_RNA) <- c("Gene","TMT","TPM")
  412. TMT_RNA$log2_TMT <- log2(TMT_RNA$TMT)
  413. TMT_RNA$log2_TPM <- log2(TMT_RNA$TPM)
  415. TMT_RNA[sapply(TMT_RNA, is.infinite)] <- NA
  416. #TMT_RNA <- na.omit(TMT_RNA) #went from 1105 to 1003!
  418. map <- ggplot(TMT_RNA, aes(x=log2_TPM,y=log2_TMT))+geom_point(size=0.2) +
  419. labs(x = "log2(TPM)", y = "log2(TMT signal)") +
  420. theme_bw() +
  421. theme(axis.text = element_text(size=12),
  422. axis.title = element_text(size=14),
  423. aspect.ratio=1) +
  424. stat_smooth(method=lm)
  425. ggsave("TMT_TPM.pdf", map, width = 3, height = 3, unit = "in")
  428. cor_TMT_RNA <- cor(TMT_RNA[,c(4:5)], method = 'pearson', use = "pairwise.complete.obs") #correlation is 0.0999
  431. ##Rank rank proteomics and RNAseq
  433. TMT_RNA <- na.omit(TMT_RNA)
  434. TMT_RNA <- TMT_RNA[order(-TMT_RNA$TMT),]
  435. TMT_RNA$TMT_rank <- seq.int(nrow(TMT_RNA))
  436. TMT_RNA <- TMT_RNA[order(-TMT_RNA$TPM),]
  437. TMT_RNA$TPM_rank <- seq.int(nrow(TMT_RNA))
  439. map <- ggplot(TMT_RNA, aes(x=TPM_rank,y=TMT_rank))+geom_point(size=0.2) +
  440. labs(x = "RNA rank", y = "Protein rank") +
  441. theme_bw() +
  442. theme(axis.text = element_text(size=12),
  443. axis.title = element_text(size=14),
  444. aspect.ratio=1) +
  445. stat_smooth(method=lm)
  447. cor_TMT_RNA_rank <- cor(TMT_RNA[,c(6:7)], method = 'pearson', use = "pairwise.complete.obs") #correlation is 0.108
  449. ggsave("TMT_TPM rank rank.pdf", map, width = 3, height = 3, unit = "in")
  452. #Hypergeometric plot rank-rank
  453. #BiocManager::install("RRHO")
  454. library('RRHO')
  455. invisible(RRHO(TMT_RNA[,c(1,6)], TMT_RNA[,c(1,7)], stepsize =1, alternative = "two.sided", plots = TRUE, outputdir = getwd(), labels=c("Protein rank","RNA rank")))
  459. ```
  462. Fig S10 d/e: Comparison to Caldwell et al., 2022, for FXS
  463. ```{r}
  464. #comparison to Alie MS for FXS
  466. Alie_ACM_ND <- read.csv("Caldwell_s2 ACM protein ND.csv")
  467. Alie_ACM_ND <- Alie_ACM_ND[-c(481, 483,1138),]
  468. rownames(Alie_ACM_ND) <- Alie_ACM_ND[,2]
  469. Alie_ACM_ND$WTavg <- rowSums(Alie_ACM_ND[,c(4:9)])
  470. Alie_ACM_ND$FXSavg <- rowSums(Alie_ACM_ND[,c(10:15)])
  471. Alie_ACM_ND$L2FC <- log2(Alie_ACM_ND$FXSavg / Alie_ACM_ND$WTavg)
  473. data_5_creneg_l2fc <- data_genename_working[,c(27,58)]
  474. data_5_creneg_l2fc[,2] <- log2(as.numeric(data_5_creneg_l2fc[,2]))
  475. rownames(data_5_creneg_l2fc) <- data_5_creneg_l2fc[,1]
  476. data_5_creneg_l2fc$alie <-Alie_ACM_ND[rownames(data_5_creneg_l2fc), "L2FC"]
  477. data_5_creneg_l2fc <- data_5_creneg_l2fc[,-c(1)]
  478. colnames(data_5_creneg_l2fc) <- c("Turbo","alie")
  479. data_5_creneg_l2fc <- data_5_creneg_l2fc[!is.infinite(rowSums(data_5_creneg_l2fc)),]
  480. #cor(data_5_creneg_l2fc, method = 'pearson', use = "pairwise.complete.obs")
  481. #plot(data_5_creneg_l2fc$Turbo,data_5_creneg_l2fc$alie)
  482. #sum(!is.na(data_5_creneg_l2fc$alie))
  485. map <- ggplot(data_5_creneg_l2fc, aes(x=alie,y=Turbo))+geom_point(size=0.2) +
  486. geom_vline(xintercept=0, col="red") +
  487. geom_hline(yintercept=0, col="red") +
  488. labs(x = "FXS log2 FC (in vitro)", y = "FXS log2 FC (in vivo)") +
  489. theme_bw() +
  490. theme(axis.text = element_text(size=12),
  491. axis.title = element_text(size=14),
  492. aspect.ratio=1) +
  493. stat_smooth(method=lm, se=FALSE)
  494. ggsave("corr_Alie_FXS.pdf", map, width = 3, height = 3, unit = "in")
  497. #only DEGs
  498. #data_5_creneg_l2fc_DEGs <- data_5_creneg_l2fc[rownames(as.data.frame(subset(res_crenegonly, padj<0.05))),]
  499. #cor_data_5_creneg_l2fc_DEGs <- cor(data_5_creneg_l2fc_DEGs, method = 'pearson', use = "pairwise.complete.obs")
  500. #plot(data_5_creneg_l2fc_DEGs$Cre...KO.WT.1,data_5_creneg_l2fc_DEGs$rna)
  501. ####
  502. #only DEPs
  503. rownames(data_genename) <- data_genename$DESCRIPTION
  504. data_5_creneg_l2fc_DEPs_list <- subset(data_genename_working, data_genename_working[,59]<0.05)
  505. data_5_creneg_l2fc_DEPs_list <- subset(data_5_creneg_l2fc_DEPs_list, data_5_creneg_l2fc_DEPs_list[,58]<0.8 | data_5_creneg_l2fc_DEPs_list[,58]>1.25)
  507. data_5_creneg_l2fc_DEPs <- data_5_creneg_l2fc[rownames(as.data.frame(data_5_creneg_l2fc_DEPs_list)),]
  508. cor_data_5_creneg_l2fc_DEPs <- cor(data_5_creneg_l2fc_DEPs, method = 'pearson', use = "pairwise.complete.obs")
  509. #plot(data_5_creneg_l2fc_DEPs$Turbo,data_5_creneg_l2fc_DEPs$alie)
  510. #sum(!is.na(data_5_creneg_l2fc_DEPs$alie))
  511. data_5_creneg_l2fc_DEPs$Protein <- rownames(data_5_creneg_l2fc_DEPs)
  512. data_5_creneg_l2fc_DEPs$Protein <- rownames(data_5_creneg_l2fc_DEPs)
  514. map <- ggplot(data_5_creneg_l2fc_DEPs, aes(x=alie,y=Turbo, label = Protein))+geom_point(size=0.2) +
  515. geom_vline(xintercept=0, col="red") +
  516. geom_hline(yintercept=0, col="red") +
  517. labs(x = "FXS log2 FC (in vitro)", y = "FXS log2 FC (in vivo)") +
  518. theme_bw() +
  519. theme(axis.text = element_text(size=12),
  520. axis.title = element_text(size=14),
  521. aspect.ratio=1) +
  522. stat_smooth(method=lm) +
  523. geom_text_repel(size=2,max.overlaps=10)
  524. ggsave("corr_Alie_FXS_DEPs.pdf", map, width = 3, height = 3, unit = "in")
  525. ```
  528. Fig S10 f/g: comparison to Caldwell et al., 2022, for Smad4 cKO
  529. ```{r}
  530. Alie_ACM_BMP <- read.csv("Caldwell_s2 ACM protein BMP.csv")
  531. Alie_ACM_BMP <- Alie_ACM_BMP[-c(481, 483,1138),]
  532. rownames(Alie_ACM_BMP) <- Alie_ACM_BMP[,2]
  533. Alie_ACM_BMP$WTavg <- rowSums(Alie_ACM_BMP[,c(4:9)])
  534. Alie_ACM_BMP$BMPavg <- rowSums(Alie_ACM_BMP[,c(10:15)])
  535. Alie_ACM_BMP$L2FC <- log2(Alie_ACM_BMP$BMPavg / Alie_ACM_BMP$WTavg)
  537. data_5_wtonly_l2fc <- data_genename_working[,c(27,52)]
  538. data_5_wtonly_l2fc[,2] <- log2(as.numeric(data_5_wtonly_l2fc[,2]))
  539. rownames(data_5_wtonly_l2fc) <- data_5_wtonly_l2fc[,1]
  540. data_5_wtonly_l2fc$alie <-Alie_ACM_BMP[rownames(data_5_wtonly_l2fc), "L2FC"]
  541. data_5_wtonly_l2fc <- data_5_wtonly_l2fc[,-c(1)]
  542. colnames(data_5_wtonly_l2fc) <- c("Turbo","alie")
  543. data_5_wtonly_l2fc <- data_5_wtonly_l2fc[!is.infinite(rowSums(data_5_wtonly_l2fc)),]
  544. cor_data_5_wtonly_l2fc <- cor(data_5_wtonly_l2fc, method = 'pearson', use = "pairwise.complete.obs")
  545. #plot(data_5_wtonly_l2fc$Turbo,data_5_wtonly_l2fc$alie)
  546. #sum(!is.na(data_5_wtonly_l2fc$alie))
  549. map <- ggplot(data_5_wtonly_l2fc, aes(x=alie,y=Turbo))+geom_point(size=0.2) +
  550. geom_vline(xintercept=0, col="red") +
  551. geom_hline(yintercept=0, col="red") +
  552. labs(x = "+BMP6 log2 FC (in vitro)", y = "Smad4cKO log2 FC (in vivo)") +
  553. theme_bw() +
  554. theme(axis.text = element_text(size=12),
  555. axis.title = element_text(size=14),
  556. aspect.ratio=1) +
  557. stat_smooth(method=lm, se=FALSE)
  558. ggsave("corr_Alie_BMP.pdf", map, width = 3, height = 3, unit = "in")
  560. #only DEPs
  561. rownames(data_genename) <- data_genename$DESCRIPTION
  563. data_5_wtonly_l2fc_DEPs_list <- subset(data_genename_working, data_genename_working[,53]<0.05)
  564. data_5_wtonly_l2fc_DEPs_list <- subset(data_5_wtonly_l2fc_DEPs_list, data_5_wtonly_l2fc_DEPs_list[,52]<0.8 | data_5_wtonly_l2fc_DEPs_list[,52]>1.25)
  567. data_5_wtonly_l2fc_DEPs <- data_5_wtonly_l2fc[rownames(as.data.frame(data_5_wtonly_l2fc_DEPs_list)),]
  568. cor_data_5_wtonly_l2fc_DEPs <- cor(data_5_wtonly_l2fc_DEPs, method = 'pearson', use = "pairwise.complete.obs")
  569. #plot(data_5_wtonly_l2fc_DEPs$Turbo,data_5_wtonly_l2fc_DEPs$alie)
  570. #sum(!is.na(data_5_wtonly_l2fc_DEPs$alie))
  571. data_5_wtonly_l2fc_DEPs$Protein <- rownames(data_5_wtonly_l2fc_DEPs)
  573. map <- ggplot(data_5_wtonly_l2fc_DEPs, aes(x=alie,y=Turbo, label=Protein))+geom_point(size=0.2) +
  574. geom_vline(xintercept=0, col="red") +
  575. geom_hline(yintercept=0, col="red") +
  576. labs(x = "+BMP6 log2 FC (in vitro)", y = "Smad4cKO log2 FC (in vivo)") +
  577. theme_bw() +
  578. theme(axis.text = element_text(size=12),
  579. axis.title = element_text(size=14),
  580. aspect.ratio=1) +
  581. stat_smooth(method=lm) +
  582. geom_text_repel(size=2,max.overlaps=10)
  583. ggsave("corr_Alie_BMP_DEPs.pdf", map, width = 3, height = 3, unit = "in")
  585. ```
  588. Supp table 7: Filtering for DEPs
  589. ```{r}
  590. #List of genes up and down in FXS
  592. library(dplyr)
  593. data_genename_working <- read.csv("NAJD-1-16_v2_jd.csv")
  594. data_genename_working[,27] <- gsub(".*GN\\=", "", data_genename_working[,27])
  595. data_genename_working[,27] <- gsub(" PE.*", "", data_genename_working[,27])
  596. data_genename_working <- distinct(data_genename_working,DESCRIPTION, .keep_all=TRUE)
  598. data_genename_working <- data_genename_working[!grepl("contaminant",data_genename_working$LOCUS),]
  599. data_genename_working <- data_genename_working[!grepl("Streptavidin",data_genename_working$DESCRIPTION),]
  600. data_genename_working$AvgTurbo <- rowMeans(data_genename_working[,c(28:39)])
  601. data_genename_working$AvgsmFP <- rowMeans(data_genename_working[,c(40:43)])
  602. data_genename_working$Turbo_over_smFP <- data_genename_working$AvgTurbo/data_genename_working$AvgsmFP
  605. data_fxs_genes <- data_genename_working[data_genename_working[,c(59)]<0.05,]
  606. data_fxs_genes <- data_fxs_genes[,c(27,1,58,59)]
  607. data_fxs_genes <- data_fxs_genes[order(data_fxs_genes$Cre...KO.WT.pvalue.1),]
  608. colnames(data_fxs_genes) <- c( "Protein", "Locus","Fold Change","p-value")
  609. data_fxs_genes[,c(3)] <- round(as.numeric(data_fxs_genes[,c(3)]), digits=3)
  612. data_fxs_genes_up <- data_fxs_genes[data_fxs_genes[,c(3)]>1,]
  613. data_fxs_genes_up <- subset(data_fxs_genes_up, data_fxs_genes_up$`Fold Change` > 1.25)
  615. data_fxs_genes_down <- data_fxs_genes[data_fxs_genes[,c(3)]<1,]
  616. data_fxs_genes_down <- subset(data_fxs_genes_down, data_fxs_genes_down$`Fold Change` < 0.80)
  619. #List of genes up and down in Smad4cKO WT
  621. data_Smad4cKO_genes <- data_genename_working[data_genename_working[,c(53)]<0.05,]
  622. data_Smad4cKO_genes <- data_Smad4cKO_genes[,c(27,1,52,53)]
  623. data_Smad4cKO_genes <- data_Smad4cKO_genes[order(data_Smad4cKO_genes$WT.Cre.....pvalue),]
  624. colnames(data_Smad4cKO_genes) <- c( "Protein", "Locus","Fold Change","p-value")
  625. data_Smad4cKO_genes <- data_Smad4cKO_genes[-c(1),]
  626. data_Smad4cKO_genes[,c(3)] <- round(as.numeric(data_Smad4cKO_genes[,c(3)]), digits=3)
  629. data_Smad4cKO_genes_up <- data_Smad4cKO_genes[data_Smad4cKO_genes[,c(3)]>1,]
  630. data_Smad4cKO_genes_up <- subset(data_Smad4cKO_genes_up, data_Smad4cKO_genes_up$`Fold Change` > 1.25)
  632. data_Smad4cKO_genes_down <- data_Smad4cKO_genes[data_Smad4cKO_genes[,c(3)]<1,]
  633. data_Smad4cKO_genes_down <- subset(data_Smad4cKO_genes_down, data_Smad4cKO_genes_down$`Fold Change` < 0.80)
  637. #List of genes up and down in Smad4cKO KO
  638. data_Smad4cKO_in_KO_genes <- data_genename_working[data_genename_working[,c(55)]<0.05,]
  639. data_Smad4cKO_in_KO_genes <- data_Smad4cKO_in_KO_genes[,c(27,1,54,55)]
  640. data_Smad4cKO_in_KO_genes <- data_Smad4cKO_in_KO_genes[order(data_Smad4cKO_in_KO_genes$KO.Cre.....pvalue),]
  641. colnames(data_Smad4cKO_in_KO_genes) <- c( "Protein", "Locus","Fold Change","p-value")
  642. data_Smad4cKO_in_KO_genes[,c(3)] <- round(as.numeric(data_Smad4cKO_in_KO_genes[,c(3)]), digits=3)
  645. data_Smad4cKO_in_KO_genes_up <- data_Smad4cKO_in_KO_genes[data_Smad4cKO_in_KO_genes[,c(3)]>1,]
  646. data_Smad4cKO_in_KO_genes_up <- subset(data_Smad4cKO_in_KO_genes_up, data_Smad4cKO_in_KO_genes_up$`Fold Change` > 1.25)
  648. data_Smad4cKO_in_KO_genes_down <- data_Smad4cKO_in_KO_genes[data_Smad4cKO_in_KO_genes[,c(3)]<1,]
  649. data_Smad4cKO_in_KO_genes_down <- subset(data_Smad4cKO_in_KO_genes_down, data_Smad4cKO_in_KO_genes_down$`Fold Change` < 0.80)
  652. #List of genes up and down in KOKO over WTWT
  654. data_kokowtwt_genes <- data_genename_working[data_genename_working[,c(61)]<0.05,]
  655. data_kokowtwt_genes <- data_kokowtwt_genes[,c(27,1,60,61)]
  656. data_kokowtwt_genes <- data_kokowtwt_genes[order(data_kokowtwt_genes$Cre.KO.Cre.WT.p.value),]
  657. colnames(data_kokowtwt_genes) <- c( "Protein", "Locus","Fold Change","p-value")
  659. data_kokowtwt_genes[,c(3)] <- round(as.numeric(data_kokowtwt_genes[,c(3)]), digits=3)
  662. data_kokowtwt_genes_up <- data_kokowtwt_genes[data_kokowtwt_genes[,c(3)]>1,]
  663. data_kokowtwt_genes_up <- subset(data_kokowtwt_genes_up, data_kokowtwt_genes_up$`Fold Change` > 1.25)
  665. data_kokowtwt_genes_down <- data_kokowtwt_genes[data_kokowtwt_genes[,c(3)]<1,]
  666. data_kokowtwt_genes_down <- subset(data_kokowtwt_genes_down, data_kokowtwt_genes_down$`Fold Change` < 0.80)
  669. # Making excel of all DEPs
  671. library(writexl)
  673. list_of_DEPs <- list(Comparison_1_UP = data_fxs_genes_up ,
  674. Comparison_1_DOWN = data_fxs_genes_down,
  675. Comparison_2_UP = data_Smad4cKO_in_KO_genes_up,
  676. Comparison_2_DOWN = data_Smad4cKO_in_KO_genes_down,
  677. Comparison_3_UP = data_Smad4cKO_genes_up,
  678. Comparison_3_DOWN = data_Smad4cKO_genes_down,
  679. Comparison_4_UP = data_kokowtwt_genes_up,
  680. Comparison_4_DOWN = data_kokowtwt_genes_down)
  682. write_xlsx(list_of_DEPs, path = "Supplementary Table 7.xlsx")
  685. ```
  688. Fig 5b (plotting heatmaps of DEPs in FXS)
  689. ```{r}
  690. library(pheatmap)
  691. library(matrixStats)
  692. library(viridis)
  694. rownames(data_genename_working) <- data_genename_working$DESCRIPTION
  696. FXS_up20 <- data_genename_working[data_genename_working$Cre...KO.WT.1>1.25,]
  697. FXS_up20 <- FXS_up20[order(FXS_up20$Cre...KO.WT.pvalue.1),]
  698. FXS_up20 <- FXS_up20[c(1:6),]
  700. FXS_up20_TMT <- FXS_up20[,c(28:30,34:36,66:68)]
  702. FXS_down20 <- data_genename_working[data_genename_working$Cre...KO.WT.1<0.8,]
  703. FXS_down20 <- FXS_down20[order(FXS_down20$Cre...KO.WT.pvalue.1),]
  704. FXS_down20 <- FXS_down20[c(1:20),]
  706. FXS_down20_TMT <- FXS_down20[,c(28:30,34:36,66:68)]
  708. FXS_up20_TMT$average <- rowMeans(FXS_up20_TMT[,(1:6)])
  709. FXS_up20_TMT$std <- rowSds(as.matrix(FXS_up20_TMT[,(1:6)]))
  710. FXS_up20_TMT[,(1:6)] <- (FXS_up20_TMT[,(1:6)]-FXS_up20_TMT$average)/FXS_up20_TMT$std
  711. FXS_up20_TMT_forgraph <- FXS_up20_TMT[,(1:6)]
  713. FXS_down20_TMT$average <- rowMeans(FXS_down20_TMT[,(1:6)])
  714. FXS_down20_TMT$std <- rowSds(as.matrix(FXS_down20_TMT[,(1:6)]))
  715. FXS_down20_TMT[,(1:6)] <- (FXS_down20_TMT[,(1:6)]-FXS_down20_TMT$average)/FXS_down20_TMT$std
  716. FXS_down20_TMT_forgraph <- FXS_down20_TMT[,(1:6)]
  718. ###
  720. genotype <- c("WT","WT","WT","Fmr1 KO","Fmr1 KO","Fmr1 KO")
  721. colInfo <- as.data.frame(genotype)
  722. rownames(colInfo) <- colnames(FXS_down20_TMT_forgraph)
  724. mybreaks <- c(seq(-2,2, length =30))
  726. map <- pheatmap((FXS_up20_TMT_forgraph),
  727. annotation_row = NA,
  728. annotation_col = colInfo,
  729. annotation_colors = NA,
  730. color = inferno(30),
  731. breaks = mybreaks,
  732. show_rownames = TRUE,
  733. show_colnames = FALSE,
  734. annotation_legend = TRUE,
  735. border_color = NA,
  736. fontsize_row = 6,
  737. fontsize = 8,
  738. cluster_rows = FALSE,
  739. cluster_cols = FALSE,
  740. cellwidth = 6,
  741. cellheight = 6,
  742. width = 4,
  743. height = 4,
  744. #gaps_row = c(110,205,231,247,277)
  745. )
  746. ggsave("FXS_up_20.pdf", map, width = 4, height = 4, unit = "in")
  748. map <- pheatmap((FXS_down20_TMT_forgraph),
  749. annotation_row = NA,
  750. annotation_col = colInfo,
  751. annotation_colors = NA,
  752. color = inferno(30),
  753. breaks = mybreaks,
  754. show_rownames = TRUE,
  755. show_colnames = FALSE,
  756. annotation_legend = TRUE,
  757. border_color = NA,
  758. fontsize_row = 6,
  759. fontsize = 8,
  760. cluster_rows = FALSE,
  761. cluster_cols = FALSE,
  762. cellwidth = 6,
  763. cellheight = 6,
  764. width = 4,
  765. height = 4,
  766. #gaps_row = c(110,205,231,247,277)
  767. )
  768. ggsave("FXS_down_20.pdf", map, width = 4, height = 4, unit = "in")
  773. ```
  776. Fig 5d (volcano map of DEPs in FXS)
  777. ```{r}
  779. data_forvolcano_fxs <- data_genename_working[,c(68,69,27)]
  781. colnames(data_forvolcano_fxs) <- c("L2FC","L10pvalue","Name")
  782. data_forvolcano_fxs$L2FC <- as.numeric(data_forvolcano_fxs$L2FC)
  783. data_forvolcano_fxs$L10pvalue <- as.numeric(data_forvolcano_fxs$L10pvalue)
  785. data_forvolcano_fxs$diffexpressed <- "NO"
  786. data_forvolcano_fxs$diffexpressed[data_forvolcano_fxs[,1] > 0.322 & data_forvolcano_fxs[,2] > 1.301] <- "UP"
  787. data_forvolcano_fxs$diffexpressed[data_forvolcano_fxs[,1] < -0.322 & data_forvolcano_fxs[,2] > 1.301] <- "DOWN"
  790. updowncolors <- c("blue", "red", "grey80")
  791. names(updowncolors) <- c("DOWN", "UP", "NO")
  793. data_forvolcano_fxs$label <- NA
  794. data_forvolcano_fxs$label[data_forvolcano_fxs$diffexpressed != "NO"] <- data_forvolcano_fxs$Name[data_forvolcano_fxs$diffexpressed != "NO"]
  797. map <- ggplot(data=data_forvolcano_fxs %>% arrange(label), aes(x=L2FC, y=L10pvalue, col=diffexpressed, label=label)) +
  798. geom_point(size=1) +
  799. theme_minimal() +
  800. scale_color_manual(values=updowncolors) +
  801. #geom_vline(xintercept=c(-0.32, 0.32), col="red") +
  802. geom_vline(xintercept=c(0), col="black") +
  803. geom_vline(xintercept=c(0.322), col="black", linetype='dotted') +
  804. geom_vline(xintercept=c(-0.322), col="black", linetype='dotted') +
  805. geom_hline(yintercept=c(0), col="black") +
  806. geom_hline(yintercept=-log10(0.05), col="black", linetype='dotted') +
  807. coord_cartesian(xlim=c(-2.5,2.5),ylim=c(0,3.5)) +
  808. xlab("log2 Fold Change") +
  809. ylab("log10 p value") +
  810. theme(axis.title=element_text(size=12))+
  811. guides(color=FALSE) +
  812. theme_classic()+
  813. geom_text_repel(size=2.5,max.overlaps=15)
  815. ggsave("Volcano_FXS_proteomics.pdf", map, width = 4.5, height = 3, unit = "in")
  817. ```
  820. Fig 6c: Z scores for FXS to FXS;Smad4 cKO DEPs for plotting in Prism
  821. ```{r}
  822. library(matrixStats)
  823. rownames(data_genename_working) <- data_genename_working$DESCRIPTION
  824. rownames(data_Smad4cKO_in_KO_genes_up) <- data_Smad4cKO_in_KO_genes_up$Protein
  825. Smad4cKO_in_KO_up_heatmap <- data_genename_working[rownames(data_Smad4cKO_in_KO_genes_up),]
  827. Smad4cKO_in_KO_up_heatmap$"mean" <- rowMeans(Smad4cKO_in_KO_up_heatmap[,c(9:20)])
  828. Smad4cKO_in_KO_up_heatmap$"sd" <- rowSds(as.matrix(Smad4cKO_in_KO_up_heatmap[,(9:20)]))
  829. Smad4cKO_in_KO_up_heatmap[,(28:43)] <- (Smad4cKO_in_KO_up_heatmap[,(28:43)]-Smad4cKO_in_KO_up_heatmap$mean)/Smad4cKO_in_KO_up_heatmap$sd
  831. Smad4cKO_in_KO_up_heatmap$Fmr1WTSmad4WTavg <- rowMeans(Smad4cKO_in_KO_up_heatmap[,c(28:30)])
  832. Smad4cKO_in_KO_up_heatmap$Fmr1WTSmad4cKOavg <- rowMeans(Smad4cKO_in_KO_up_heatmap[,c(31:33)])
  833. Smad4cKO_in_KO_up_heatmap$Fmr1KOSmad4WTavg <- rowMeans(Smad4cKO_in_KO_up_heatmap[,c(34:36)])
  834. Smad4cKO_in_KO_up_heatmap$Fmr1KOSmad4cKOavg <- rowMeans(Smad4cKO_in_KO_up_heatmap[,c(37:39)])
  836. write.csv(Smad4cKO_in_KO_up_heatmap, "./Smad4cKO_in_KO_up_heatmap_zscore.csv")
  839. rownames(data_Smad4cKO_in_KO_genes_down) <- data_Smad4cKO_in_KO_genes_down$Protein
  840. Smad4cKO_in_KO_down_heatmap <- data_genename_working[rownames(data_Smad4cKO_in_KO_genes_down),]
  842. Smad4cKO_in_KO_down_heatmap$"mean" <- rowMeans(Smad4cKO_in_KO_down_heatmap[,c(9:20)])
  843. Smad4cKO_in_KO_down_heatmap$"sd" <- rowSds(as.matrix(Smad4cKO_in_KO_down_heatmap[,(9:20)]))
  844. Smad4cKO_in_KO_down_heatmap[,(28:43)] <- (Smad4cKO_in_KO_down_heatmap[,(28:43)]-Smad4cKO_in_KO_down_heatmap$mean)/Smad4cKO_in_KO_down_heatmap$sd
  846. Smad4cKO_in_KO_down_heatmap$Fmr1WTSmad4WTavg <- rowMeans(Smad4cKO_in_KO_down_heatmap[,c(28:30)])
  847. Smad4cKO_in_KO_down_heatmap$Fmr1WTSmad4cKOavg <- rowMeans(Smad4cKO_in_KO_down_heatmap[,c(31:33)])
  848. Smad4cKO_in_KO_down_heatmap$Fmr1KOSmad4WTavg <- rowMeans(Smad4cKO_in_KO_down_heatmap[,c(34:36)])
  849. Smad4cKO_in_KO_down_heatmap$Fmr1KOSmad4cKOavg <- rowMeans(Smad4cKO_in_KO_down_heatmap[,c(37:39)])
  851. write.csv(Smad4cKO_in_KO_down_heatmap, "./Smad4cKO_in_KO_down_heatmap_zscore.csv")
  854. ```
  857. Supp table 8: GSEA analysis
  858. ```{r}
  859. library(msigdbr)
  860. library(enrichplot)
  861. library(ggridges)
  862. library(DOSE)
  863. library(ggplot2)
  864. library(clusterProfiler)
  866. data_genename_working <- read.csv("NAJD-1-16_v2_jd.csv")
  867. data_genename_working[,27] <- gsub(".*GN\\=", "", data_genename_working[,27])
  868. data_genename_working[,27] <- gsub(" PE.*", "", data_genename_working[,27])
  869. data_genename_working <- distinct(data_genename_working,DESCRIPTION, .keep_all=TRUE)
  871. data_genename_working <- data_genename_working[!grepl("contaminant",data_genename_working$LOCUS),]
  872. data_genename_working <- data_genename_working[!grepl("Streptavidin",data_genename_working$DESCRIPTION),]
  873. data_genename_working$AvgTurbo <- rowMeans(data_genename_working[,c(28:39)])
  874. data_genename_working$AvgsmFP <- rowMeans(data_genename_working[,c(40:43)])
  875. data_genename_working$Turbo_over_smFP <- data_genename_working$AvgTurbo/data_genename_working$AvgsmFP
  876. data_genename_working <- distinct(data_genename_working,AvgTurbo, .keep_all=TRUE) #goes from 2138 to 2012 (removes 126 proteins)
  879. GSEA_wtonly <- subset(data_genename_working,data_genename_working$Turbo_over_smFP>1.75)
  880. GSEA_wtonly$rank <- sign(as.numeric(GSEA_wtonly[,c(52)])-1) * -log10(as.numeric(GSEA_wtonly[,c(53)]))
  881. GSEA_wtonly <- GSEA_wtonly[,c(27,74)]
  882. GSEA_wtonly <- na.omit(GSEA_wtonly[order(-GSEA_wtonly$rank),])
  883. write.table(GSEA_wtonly, "./GSEA_wtonly.rnk", col.name=TRUE, sep="\t",row.names=FALSE,quote=FALSE)
  885. GSEA_koonly <- subset(data_genename_working,data_genename_working$Turbo_over_smFP>1.75)
  886. GSEA_koonly$rank <- sign(as.numeric(GSEA_koonly[,c(54)])-1) * -log10(as.numeric(GSEA_koonly[,c(55)]))
  887. GSEA_koonly <- GSEA_koonly[,c(27,74)]
  888. GSEA_koonly <- na.omit(GSEA_koonly[order(-GSEA_koonly$rank),])
  889. write.table(GSEA_koonly, "./GSEA_koonly.rnk", col.name=TRUE, sep="\t",row.names=FALSE,quote=FALSE)
  891. GSEA_crenegonly <- subset(data_genename_working,data_genename_working$Turbo_over_smFP>1.75)
  892. GSEA_crenegonly$rank <- sign(as.numeric(GSEA_crenegonly[,c(58)])-1) * -log10(as.numeric(GSEA_crenegonly[,c(59)]))
  893. GSEA_crenegonly <- GSEA_crenegonly[,c(27,74)]
  894. GSEA_crenegonly <- na.omit(GSEA_crenegonly[order(-GSEA_crenegonly$rank),])
  895. write.table(GSEA_crenegonly, "./GSEA_crenegonly.rnk", col.name=TRUE, sep="\t",row.names=FALSE,quote=FALSE)
  897. GSEA_kokowtwt <- subset(data_genename_working,data_genename_working$Turbo_over_smFP>1.75)
  898. GSEA_kokowtwt$rank <- sign(as.numeric(GSEA_kokowtwt[,c(60)])-1) * -log10(as.numeric(GSEA_kokowtwt[,c(61)]))
  899. GSEA_kokowtwt <- GSEA_kokowtwt[,c(27,74)]
  900. GSEA_comp5 <- na.omit(GSEA_kokowtwt[order(-GSEA_kokowtwt$rank),])
  901. write.table(GSEA_kokowtwt, "./GSEA_kokowtwt.rnk", col.name=TRUE, sep="\t",row.names=FALSE,quote=FALSE)
  905. GSEA_wtonly_forR <- GSEA_wtonly$rank
  906. names(GSEA_wtonly_forR) <- GSEA_wtonly$DESCRIPTION
  908. GSEA_koonly_forR <- GSEA_koonly$rank
  909. names(GSEA_koonly_forR) <- GSEA_koonly$DESCRIPTION
  911. GSEA_crenegonly_forR <- GSEA_crenegonly$rank
  912. names(GSEA_crenegonly_forR) <- GSEA_crenegonly$DESCRIPTION
  914. GSEA_comp5_forR <- GSEA_comp5$rank
  915. names(GSEA_comp5_forR) <- GSEA_comp5$DESCRIPTION
  918. gene_sets_h = msigdbr(species = "mouse", category = "H") #hallmark
  919. gene_sets_cp = msigdbr(species = "mouse", category = "C2") #canonical pathways including biocarta, reactome, wikipathways
  920. gene_sets_go = msigdbr(species = "mouse", category = "C5") #go including mf, cc, bp
  922. msigdbr_t2g_h = gene_sets_h %>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()
  923. msigdbr_t2g_cp = gene_sets_cp %>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()
  924. msigdbr_t2g_go = gene_sets_go %>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()
  927. GSEA_wtonly_h <- GSEA(gene = GSEA_wtonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_h)
  928. GSEA_koonly_h <- GSEA(gene = GSEA_koonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_h)
  929. GSEA_crenegonly_h <- GSEA(gene = GSEA_crenegonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_h)
  930. GSEA_comp5_h <- GSEA(gene = GSEA_comp5_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_h)
  932. GSEA_wtonly_h_df <- as.data.frame(GSEA_wtonly_h)
  933. GSEA_wtonly_h_df[,c(4,5)] <- round(GSEA_wtonly_h_df[,c(4,5)], digits=3)
  936. GSEA_koonly_h_df <- as.data.frame(GSEA_koonly_h)
  937. GSEA_koonly_h_df[,c(4,5)] <- round(GSEA_koonly_h_df[,c(4,5)], digits=3)
  939. GSEA_crenegonly_h_df <- as.data.frame(GSEA_crenegonly_h)
  940. GSEA_crenegonly_h_df[,c(4,5)] <- round(GSEA_crenegonly_h_df[,c(4,5)], digits=3)
  943. GSEA_comp5_h_df <- as.data.frame(GSEA_comp5_h)
  944. GSEA_comp5_h_df[,c(4,5)] <- round(GSEA_comp5_h_df[,c(4,5)], digits=3)
  947. GSEA_wtonly_h_df_up <- GSEA_wtonly_h_df[GSEA_wtonly_h_df$NES>0,-c(1)]
  948. GSEA_wtonly_h_df_down <- GSEA_wtonly_h_df[GSEA_wtonly_h_df$NES<0,-c(1)]
  949. GSEA_koonly_h_df_up <- GSEA_koonly_h_df[GSEA_koonly_h_df$NES>0,-c(1)]
  950. GSEA_koonly_h_df_down <- GSEA_koonly_h_df[GSEA_koonly_h_df$NES<0,-c(1)]
  951. GSEA_crenegonly_h_df_up <- GSEA_crenegonly_h_df[GSEA_crenegonly_h_df$NES>0,-c(1)]
  952. GSEA_crenegonly_h_df_down <- GSEA_crenegonly_h_df[GSEA_crenegonly_h_df$NES<0,-c(1)]
  953. GSEA_comp5_h_df_up <- GSEA_comp5_h_df[GSEA_comp5_h_df$NES>0,-c(1)]
  954. GSEA_comp5_h_df_down <- GSEA_comp5_h_df[GSEA_comp5_h_df$NES<0,-c(1)]
  956. GSEA_wtonly_cp <- GSEA(gene = GSEA_wtonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_cp)
  957. GSEA_koonly_cp <- GSEA(gene = GSEA_koonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_cp)
  958. GSEA_crenegonly_cp <- GSEA(gene = GSEA_crenegonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_cp)
  959. GSEA_comp5_cp <- GSEA(gene = GSEA_comp5_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_cp)
  961. GSEA_wtonly_cp_df <- as.data.frame(GSEA_wtonly_cp)
  962. GSEA_wtonly_cp_df[,c(4,5)] <- round(GSEA_wtonly_cp_df[,c(4,5)], digits=3)
  965. GSEA_koonly_cp_df <- as.data.frame(GSEA_koonly_cp)
  966. GSEA_koonly_cp_df[,c(4,5)] <- round(GSEA_koonly_cp_df[,c(4,5)], digits=3)
  969. GSEA_crenegonly_cp_df <- as.data.frame(GSEA_crenegonly_cp)
  970. GSEA_crenegonly_cp_df[,c(4,5)] <- round(GSEA_crenegonly_cp_df[,c(4,5)], digits=3)
  973. GSEA_comp5_cp_df <- as.data.frame(GSEA_comp5_cp)
  974. GSEA_comp5_cp_df[,c(4,5)] <- round(GSEA_comp5_cp_df[,c(4,5)], digits=3)
  977. library(tidyverse)
  978. strings <- c("KEGG_","REACTOME_","WP_","BIOCARTA_")
  980. GSEA_wtonly_cp_df_up <- GSEA_wtonly_cp_df[GSEA_wtonly_cp_df$NES>0,-c(1)]
  981. GSEA_wtonly_cp_df_up <- GSEA_wtonly_cp_df_up %>%
  982. filter(str_detect(GSEA_wtonly_cp_df_up$Description, paste(strings,collapse= "|")))
  984. GSEA_wtonly_cp_df_down <- GSEA_wtonly_cp_df[GSEA_wtonly_cp_df$NES<0,-c(1)]
  985. GSEA_wtonly_cp_df_down <- GSEA_wtonly_cp_df_down %>%
  986. filter(str_detect(GSEA_wtonly_cp_df_down$Description, paste(strings,collapse= "|")))
  988. GSEA_koonly_cp_df_up <- GSEA_koonly_cp_df[GSEA_koonly_cp_df$NES>0,-c(1)]
  989. GSEA_koonly_cp_df_up <- GSEA_koonly_cp_df_up %>%
  990. filter(str_detect(GSEA_koonly_cp_df_up$Description, paste(strings,collapse= "|")))
  992. GSEA_koonly_cp_df_down <- GSEA_koonly_cp_df[GSEA_koonly_cp_df$NES<0,-c(1)]
  993. GSEA_koonly_cp_df_down <- GSEA_koonly_cp_df_down %>%
  994. filter(str_detect(GSEA_koonly_cp_df_down$Description, paste(strings,collapse= "|")))
  997. GSEA_crenegonly_cp_df_up <- GSEA_crenegonly_cp_df[GSEA_crenegonly_cp_df$NES>0,-c(1)]
  998. GSEA_crenegonly_cp_df_up <- GSEA_crenegonly_cp_df_up %>%
  999. filter(str_detect(GSEA_crenegonly_cp_df_up$Description, paste(strings,collapse= "|")))
  1001. GSEA_crenegonly_cp_df_down <- GSEA_crenegonly_cp_df[GSEA_crenegonly_cp_df$NES<0,-c(1)]
  1002. GSEA_crenegonly_cp_df_down <- GSEA_crenegonly_cp_df_down %>%
  1003. filter(str_detect(GSEA_crenegonly_cp_df_down$Description, paste(strings,collapse= "|")))
  1005. GSEA_comp5_cp_df_up <- GSEA_comp5_cp_df[GSEA_comp5_cp_df$NES>0,-c(1)]
  1006. GSEA_comp5_cp_df_up <- GSEA_comp5_cp_df_up %>%
  1007. filter(str_detect(GSEA_comp5_cp_df_up$Description, paste(strings,collapse= "|")))
  1009. GSEA_comp5_cp_df_down <- GSEA_comp5_cp_df[GSEA_comp5_cp_df$NES<0,-c(1)]
  1010. GSEA_comp5_cp_df_down <- GSEA_comp5_cp_df_down %>%
  1011. filter(str_detect(GSEA_comp5_cp_df_down$Description, paste(strings,collapse= "|")))
  1013. GSEA_wtonly_go <- GSEA(gene = GSEA_wtonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_go)
  1014. GSEA_koonly_go <- GSEA(gene = GSEA_koonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1, TERM2GENE = msigdbr_t2g_go)
  1015. GSEA_crenegonly_go <- GSEA(gene = GSEA_crenegonly_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_go)
  1016. GSEA_comp5_go <- GSEA(gene = GSEA_comp5_forR, pAdjustMethod = "BH", pvalueCutoff = 1,TERM2GENE = msigdbr_t2g_go)
  1018. GSEA_wtonly_go_df <- as.data.frame(GSEA_wtonly_go)
  1019. GSEA_wtonly_go_df[,c(4,5)] <- round(GSEA_wtonly_go_df[,c(4,5)], digits=3)
  1022. GSEA_koonly_go_df <- as.data.frame(GSEA_koonly_go)
  1023. GSEA_koonly_go_df[,c(4,5)] <- round(GSEA_koonly_go_df[,c(4,5)], digits=3)
  1026. GSEA_crenegonly_go_df <- as.data.frame(GSEA_crenegonly_go)
  1027. GSEA_crenegonly_go_df[,c(4,5)] <- round(GSEA_crenegonly_go_df[,c(4,5)], digits=3)
  1029. GSEA_comp5_go_df <- as.data.frame(GSEA_comp5_go)
  1030. GSEA_comp5_go_df[,c(4,5)] <- round(GSEA_comp5_go_df[,c(4,5)], digits=3)
  1034. GSEA_wtonly_go_df_up <- GSEA_wtonly_go_df[GSEA_wtonly_go_df$NES>0,-c(1)]
  1035. GSEA_wtonly_go_df_down <- GSEA_wtonly_go_df[GSEA_wtonly_go_df$NES<0,-c(1)]
  1036. GSEA_koonly_go_df_up <- GSEA_koonly_go_df[GSEA_koonly_go_df$NES>0,-c(1)]
  1037. GSEA_koonly_go_df_down <- GSEA_koonly_go_df[GSEA_koonly_go_df$NES<0,-c(1)]
  1038. GSEA_crenegonly_go_df_up <- GSEA_crenegonly_go_df[GSEA_crenegonly_go_df$NES>0,-c(1)]
  1039. GSEA_crenegonly_go_df_down <- GSEA_crenegonly_go_df[GSEA_crenegonly_go_df$NES<0,-c(1)]
  1040. GSEA_comp5_go_df_up <- GSEA_wtonly_go_df[GSEA_comp5_go_df$NES>0,-c(1)]
  1041. GSEA_comp5_go_df_down <- GSEA_wtonly_go_df[GSEA_comp5_go_df$NES<0,-c(1)]
  1043. library(writexl)
  1045. list_of_gsea <- list(Comparison_1_Hallmark_UP = GSEA_crenegonly_h_df_up,
  1046. Comparison_1_Hallmark_DOWN = GSEA_crenegonly_h_df_down,
  1047. Comparison_2_Hallmark_UP = GSEA_koonly_h_df_up,
  1048. Comparison_2_Hallmark_DOWN = GSEA_koonly_h_df_down,
  1049. Comparison_3_Hallmark_UP = GSEA_wtonly_h_df_up,
  1050. Comparison_3_Hallmark_DOWN = GSEA_wtonly_h_df_down,
  1051. Comparison_4_Hallmark_UP = GSEA_comp5_h_df_up,
  1052. Comparison_4_Hallmark_DOWN = GSEA_comp5_h_df_down,
  1054. Comparison_1_CP_UP = GSEA_crenegonly_cp_df_up,
  1055. Comparison_1_CP_DOWN = GSEA_crenegonly_cp_df_down,
  1056. Comparison_2_CP_UP = GSEA_koonly_cp_df_up,
  1057. Comparison_2_CP_DOWN = GSEA_koonly_cp_df_down,
  1058. Comparison_3_CP_UP = GSEA_wtonly_cp_df_up,
  1059. Comparison_3_CP_DOWN = GSEA_wtonly_cp_df_down,
  1060. Comparison_4_CP_UP = GSEA_comp5_cp_df_up,
  1061. Comparison_4_CP_DOWN = GSEA_comp5_cp_df_down,
  1063. Comparison_1_GO_UP = GSEA_crenegonly_go_df_up,
  1064. Comparison_1_GO_DOWN = GSEA_crenegonly_go_df_down,
  1065. Comparison_2_GO_UP = GSEA_koonly_go_df_up,
  1066. Comparison_2_GO_DOWN = GSEA_koonly_go_df_down,
  1067. Comparison_3_GO_UP = GSEA_wtonly_go_df_up,
  1068. Comparison_3_GO_DOWN = GSEA_wtonly_go_df_down,
  1069. Comparison_4_GO_UP = GSEA_comp5_go_df_up,
  1070. Comparison_4_GO_DOWN = GSEA_comp5_go_df_down
  1071. )
  1073. write_xlsx(list_of_gsea, path = "Supplementary Table 8.xlsx")
  1076. ```
  1079. Fig 6d: Plot of top hallmark pathways from GSEA analysis
  1080. ```{r}
  1081. library('dplyr')
  1082. library('stringr')
  1083. library('ggplot2')
  1086. GSEA_crenegonly_h_df$Comparison <- 1
  1087. GSEA_koonly_h_df$Comparison <- 2
  1088. GSEA_comp5_h_df$Comparison <- 4
  1090. hallmark_all <- rbind(GSEA_crenegonly_h_df,GSEA_koonly_h_df,GSEA_comp5_h_df)
  1093. hallmark_all <- hallmark_all %>%
  1094. group_by(Comparison,sign(NES)) %>%
  1095. arrange((-sign(NES)*NES), .by_group=TRUE) %>%
  1096. dplyr::slice(1:3)
  1098. hallmark_all$Description <- str_wrap(hallmark_all$Description, width = 40)
  1099. hallmark_all <- hallmark_all[order(hallmark_all$NES),]
  1100. hallmark_all$Comparison <- factor(hallmark_all$Comparison,levels=c(1,2,4))
  1102. hallmark_all$Description <- gsub("^.*?_","",hallmark_all$Description)
  1105. labels <- c('Comparison 1', 'Comparison 2', 'Comparison 4')
  1106. names(labels) <- c(1,2,4)
  1108. hallmark_all %>%
  1109. mutate(ordering = as.numeric(Comparison) + NES,
  1110. Description = fct_reorder(Description, ordering)) %>%
  1111. mutate(Description = factor(Description),
  1112. Description = factor(Description, levels = rev(levels(Description))))%>%
  1113. ggplot(aes(x=NES, y=Description, fill = NES)) +
  1114. geom_bar(stat = 'identity') +
  1115. ggtitle("")+
  1116. facet_grid(~Comparison, labeller = labeller(Comparison = labels))+
  1117. xlab("Normalized Enrichment Score") + ylab("Hallmark Pathways")+
  1118. scale_fill_gradient2(low = "blue",midpoint = 0, mid = "white", high ="red") +
  1119. #theme_base(base_size = 16) +
  1120. coord_cartesian(xlim = c(-3,3))+
  1121. theme(axis.text.x = element_text(size = 12),
  1122. axis.title.y = element_text(size = 18)) +
  1123. geom_vline(xintercept = 0, color = "black", linetype = "dashed", linewidth = .5)
  1125. ggsave("Hallmark_TopInAll.pdf", height = 3, width = 8)
  1126. ```
  1129. Fig 6F: Z-scores for proteins in the protein secretion pathway (output used to create plots in Prism)
  1130. ```{r}
  1132. secretion_proteins <- as.data.frame(gene_sets_h[gene_sets_h$gs_name=="HALLMARK_PROTEIN_SECRETION",])
  1133. secretion_Alie_BMP <- Alie_ACM_BMP[secretion_proteins$gene_symbol,]
  1134. secretion_Alie_BMP <- na.omit(secretion_Alie_BMP) #96 to 25
  1136. secretion_Alie_BMP <- secretion_Alie_BMP[order(secretion_Alie_BMP[,2]),]
  1137. secretion_Alie_BMP$"mean" <- rowMeans(secretion_Alie_BMP[,c(4:15)])
  1138. secretion_Alie_BMP$"sd" <- rowSds(as.matrix(secretion_Alie_BMP[,(4:15)]))
  1139. secretion_Alie_BMP[,(4:15)] <- (secretion_Alie_BMP[,(4:15)]-secretion_Alie_BMP$mean)/secretion_Alie_BMP$sd
  1140. secretion_Alie_BMP$WT <- rowMeans(secretion_Alie_BMP[,c(4:9)])
  1141. secretion_Alie_BMP$BMP6 <- rowMeans(secretion_Alie_BMP[,c(10:15)])
  1142. secretion_Alie_BMP$up_down <- (secretion_Alie_BMP[,21]-secretion_Alie_BMP[,22])>0
  1143. secretion_Alie_BMP <- secretion_Alie_BMP[order(-secretion_Alie_BMP[,22]),]
  1145. write.csv(secretion_Alie_BMP, "./secretion_Alie_BMP.csv")
  1148. ```
  1151. Fig S12 and 6E: average Z-scores of secretory proteins (output used to create plots in Prism)
  1152. ```{r}
  1153. #Heatmap of secretory pathway proteins
  1154. secretory_creneg <- read.csv("secretion proteomics creneg.csv") #from MSigDB MM3876
  1155. rownames(secretory_creneg) <- secretory_creneg[,2]
  1156. secretory_heatmap <- data_genename[rownames(secretory_creneg),]
  1157. secretory_heatmap <- secretory_heatmap[order(as.numeric(secretory_heatmap[,55])),]
  1158. secretory_heatmap$"mean" <- rowMeans(secretory_heatmap[,c(9:20)])
  1159. secretory_heatmap$"sd" <- rowSds(as.matrix(secretory_heatmap[,(9:20)]))
  1160. secretory_heatmap[,(28:43)] <- (secretory_heatmap[,(28:43)]-secretory_heatmap$mean)/secretory_heatmap$sd
  1162. secretory_heatmap$Fmr1WTSmad4WTavg <- rowMeans(secretory_heatmap[,c(28:30)])
  1163. secretory_heatmap$Fmr1WTSmad4cKOavg <- rowMeans(secretory_heatmap[,c(31:33)])
  1164. secretory_heatmap$Fmr1KOSmad4WTavg <- rowMeans(secretory_heatmap[,c(34:36)])
  1165. secretory_heatmap$Fmr1KOSmad4cKOavg <- rowMeans(secretory_heatmap[,c(37:39)])
  1168. write.csv(secretory_heatmap, "./secretory_heatmap_zscore.csv")
  1171. ```
  1174. Session Info: packages and versions
  1175. ```{r}
  1176. sessionInfo()
  1177. ```
  1180. This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
  1182. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
  1184. Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.
  1186. When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).
  1188. The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

Proteomic_Analysis_Dengetal.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: James Deng1,2,3, Adrien Paumier1, Lara Labarta-Bajo1, Ashley N Brandebura1, Nick A Andrews4, Samuel B Kahn5, Reina Bassil1,5, Tao Tao1, Antonio F M Pinto6,7, Jolene K Diedrich6,7, Nicola J Allen1
ORCID iDs: James Deng, Adrien Paumier, Lara Labarta-Bajo, Ashley N Brandebura, Nick A Andrews, Samuel B Kahn, Reina Bassil, Tao Tao, Antonio F M Pinto, Jolene K Diedrich, Nicola J Allen
  1. Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA USA
  2. Medical Scientist Training Program, University of California, San Diego, La Jolla, CA USA
  3. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA USA
  4. In Vivo Scientific Services, Salk Institute for Biological Studies, La Jolla, CA USA
  5. Department of Biology, University of California, San Diego, La Jolla, CA USA
  6. Mass Spectrometry Core for Proteomics and Metabolomics, Salk Institute for Biological Studies, La Jolla, CA USA
  7. Multi-Omics Core, The Scripps Research Institute, La Jolla, CA USA
Institutions: Salk Institute for Biological Studies (United States); University of California San Diego (United States); Scripps Research Institute (United States)
Journal: Nature communications, volume 17, issue 1, article 5603
Dates: received 2 June 2024; accepted 31 March 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71919-6 · PMID 42020399 · PMCID PMC13314986 · OpenAlex W7155183094
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), other condition (population), autism (population), cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Evoked potentials, Connectivity, Smoothing, state filtering, decompositions
Keywords: Astrocyte, Autism spectrum disorders
MeSH: Astrocytes*, Bone Morphogenetic Proteins*, Fragile X Syndrome*, Animals, Auditory Cortex, Disease Models, Animal, Fragile X Messenger Ribonucleoprotein 1, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Proteomics, Signal Transduction, Smad4 Protein (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NICHD NIH HHS (F30 HD106699); NINDS NIH HHS (R21 NS137659)
Citations: not cited yet (Europe PMC); 135 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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Zenodo 18826425

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 7 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (2 files), ggplot2 (2 files), pheatmap (2 files), tidyverse (2 files), DESeq2 (1 file), STAR (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
5 files
At the source: zenodo.org/records/18826425

Zenodo 18180000

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 7 files, 2 scripts
Software Heritage: not checked
Found in: DataCite
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (2 files), ggplot2 (2 files), pheatmap (2 files), tidyverse (2 files), DESeq2 (1 file), STAR (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.18180000

Code availability statement

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Tracing map

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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-71919-6.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 14 MeSH terms, 2 funders, 133 references.

Cite

This paper

Deng, J., Paumier, A., Labarta-Bajo, L., Brandebura, A. N., Andrews, N. A., Kahn, S. B., Bassil, R., Tao, T., Pinto, A. F. M., Diedrich, J. K., & Allen, N. J. (2026). Suppression of astrocyte BMP signaling improves molecular signatures and functional deficits in a fragile X syndrome mouse model. Nature communications, 17(1), 5603. https://doi.org/10.1038/s41467-026-71919-6

BibTeX

@article{deng2026suppression,
author = {Deng, James and Paumier, Adrien and Labarta-Bajo, Lara and Brandebura, Ashley N and Andrews, Nick A and Kahn, Samuel B and Bassil, Reina and Tao, Tao and Pinto, Antonio F M and Diedrich, Jolene K and Allen, Nicola J},
title = {{Suppression of astrocyte BMP signaling improves molecular signatures and functional deficits in a fragile X syndrome mouse model}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5603},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71919-6},
url = {https://doi.org/10.1038/s41467-026-71919-6},
pmid = {42020399},
pmcid = {PMC13314986}
}

RIS

TY - JOUR
AU - Deng, James
AU - Paumier, Adrien
AU - Labarta-Bajo, Lara
AU - Brandebura, Ashley N
AU - Andrews, Nick A
AU - Kahn, Samuel B
AU - Bassil, Reina
AU - Tao, Tao
AU - Pinto, Antonio F M
AU - Diedrich, Jolene K
AU - Allen, Nicola J
TI - Suppression of astrocyte BMP signaling improves molecular signatures and functional deficits in a fragile X syndrome mouse model
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/23
VL - 17
IS - 1
SP - 5603
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71919-6
UR - https://doi.org/10.1038/s41467-026-71919-6
LA - en
ER -

CSL-JSON

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