OSCR

TYK2 mediates neuroinflammation in Alzheimer's disease brains with TDP-43 pathology.

Code ↔ Paper

10 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 10 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Increased interferon signaling in AD ↔ ad_vs_healthy_gsea.Rmd, lines 35–166 · score 0.97 · Mount Sinai Brain, inferior frontal gyrus, superior temporal gyrus, frontal pole, parahippocampal gyrus, prefrontal cortex
  2. [2] § Methods › Differential gene expression analysis of brain regions ↔ ad_vs_healthy_gsea.Rmd, lines 35–166 · score 0.95 · inferior frontal gyrus, superior temporal gyrus, frontal pole, parahippocampal gyrus, clinical diagnosis, prefrontal cortex
  3. [3] § Methods › DRIAD-SP: RNA-sequencing data processing and TDP-43 status prediction ↔ tdp43_prediction/tdp43_rosmap.R, lines 62–127 · score 0.71 · UNC13A CE1, UNC13A CE2, TPM, STMN2, prediction, transcripts
  4. [4] § Results › DRIAD-SP predicts efficacy of blocking interferon signaling ↔ tdp43_prediction/tdp43_rosmap.R, lines 62–127 · score 0.67 · UNC13A CE1, UNC13A CE2, TPM, STMN2, brain region, prediction
  5. [5] § Methods › DRIAD-SP: RNA-sequencing data processing and TDP-43 status prediction ↔ job_scripts/make_salmon_index.sh, the whole file · a weak match · score 0.67 · splice variant, UNC13A, Salmon, Ensembl, STMN2
  6. [6] § Methods › DRIAD-SP: RNA-sequencing data processing and TDP-43 status prediction ↔ tdp43_prediction/tdp43_qc.R, lines 58–117 · score 0.62 · UNC13A CE1, UNC13A CE2, STMN2, prediction, transcripts, TDP
  7. [7] § Results › DRIAD-SP predicts efficacy of blocking interferon signaling ↔ tdp43_prediction/tdp43_qc.R, lines 58–117 · score 0.59 · UNC13A CE1, UNC13A CE2, STMN2, MSBB, brain region, prediction
  8. [8] § Methods › Pathway enrichment analysis ↔ ad_vs_healthy_gsea.Rmd, lines 198–331 · score 0.55 · KEGG Medicus, fgsea, Pathway, ranked, enrichment, log10
  9. [9] § Results › CRISPR screen and validation of TYK2 ↔ revision_plots.R, lines 412–460 · score 0.51 · Reactome Pathway, CRISPR screen, enriched, LFC, bars, enrichment
  10. [10] § Methods › Pathway enrichment analysis ↔ revision_plots.R, lines 412–460 · score 0.50 · Reactome_Pathways_2024, enrichR, enrichment, database, genes

Paper

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The authors' code

R Markdown · 520 lines · 13 KB · MIT · 3 matches

  1. ---
  2. title: "AD vs healthy GSEA"
  3. author: "Clemens Hug"
  4. output: html_document
  5. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  11. library(here)
  12. library(tidyverse)
  13. library(data.table)
  14. library(synExtra)
  15. library(qs)
  16. library(powerjoin)
  17. library(ggrepel)
  19. synapser::synLogin()
  20. syn <- synDownloader(normalizePath("~/data"), .cache = TRUE)
  21. ```
  23. ## Load data
  25. ```{r}
  26. base_dir <- "~/HMS Dropbox/Clemens Hug/Laura AMP-AD figures/Human YN RNA-Seq with CI Denormalized"
  29. dge_files <- list.files(
  30. base_dir,
  31. pattern = "*.csv.gz"
  32. )
  33. ```
  35. ```{r}
  36. region_map <- c(
  37. CBE = "Cerebellum",
  38. TCX = "Temporal cortex",
  39. FP = "Frontal pole",
  40. IFG = "Inferior frontal gyrus",
  41. PHG = "Parahippocampal gyrus",
  42. STG = "Superior temporal gyrus",
  43. PFC = "Prefrontal cortex"
  44. )
  46. cohort_map <- c(
  47. MayoBB = "Mayo Brain Bank",
  48. MSBB = "Mount Sinai Brain Bank",
  49. ROSMAP = "Religious Orders Study & Memory and Aging Project"
  50. )
  52. dtype_map <- c(
  53. RC = "Raw counts",
  54. CPM = "Counts per million",
  55. FPKM = "Fragments per kilobase per million"
  56. )
  58. phenotype_label <- function(x) {
  59. dplyr::recode(
  60. x,
  61. ClinicalDiagnosis = "Clinical diagnosis",
  62. CpDxAll = "Consensus pathologic diagnosis (all)",
  63. CpDxLow = "Consensus pathologic diagnosis (low confidence)",
  64. CpDxStrict= "Consensus pathologic diagnosis (strict)",
  65. Braak = "Braak NFT stage",
  66. CERAD = "CERAD neuritic plaque score",
  67. CDR = "Clinical Dementia Rating",
  68. MMSE = "Mini-Mental State Examination",
  69. .default = x
  70. )
  71. }
  73. decode_group <- function(code, phenotype) {
  74. if (is.na(code) || is.na(phenotype)) return(NA_character_)
  76. if (phenotype == "Braak") {
  77. n <- readr::parse_number(code)
  78. if (is.na(n)) return(code)
  79. return(paste0("Braak stage ", as.character(as.roman(n))))
  80. }
  82. if (phenotype == "CERAD") {
  83. n <- readr::parse_number(code)
  84. if (is.na(n)) return(code)
  85. return(paste0("CERAD C", n))
  86. }
  88. # clinical / pathologic group codes
  89. dplyr::recode(
  90. code,
  91. AD = "Alzheimer’s disease",
  92. NCI = "No cognitive impairment (control)",
  93. PA = "Pathologic aging",
  94. PSP = "Progressive supranuclear palsy",
  95. DNAD = "Dementia, non-AD",
  96. PC = "Pathologic control",
  97. .default = code
  98. )
  99. }
  101. phenotype_category <- function(x) {
  102. dplyr::case_when(
  103. x %in% c("ClinicalDiagnosis", "CDR", "MMSE") ~ "clinical",
  104. grepl("^CpDx", x) ~ "pathology (consensus)",
  105. x %in% c("Braak", "CERAD") ~ "neuropath staging",
  106. TRUE ~ "other"
  107. )
  108. }
  110. # ---- main parser ----
  111. parse_filenames <- function(files) {
  112. tibble(file = files) |>
  113. # expected structure: Cohort_Region_DataType_Phenotype_Comparison.csv
  114. tidyr::extract(
  115. file,
  116. into = c("cohort", "region_code", "data_type", "phenotype", "comparison"),
  117. regex = "^([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^.]+)\\.csv\\.gz$",
  118. remove = FALSE
  119. ) |>
  120. # split comparison like AD-NCI, B2-B1, C3-C0, etc.
  121. tidyr::separate(
  122. comparison,
  123. into = c("group1_code", "group2_code"),
  124. sep = "-",
  125. remove = FALSE,
  126. fill = "right"
  127. ) |>
  128. mutate(
  129. cohort_full = dplyr::recode(cohort, !!!cohort_map),
  130. region = dplyr::recode(region_code, !!!region_map),
  131. data_type_full = dplyr::recode(data_type, !!!dtype_map),
  132. phenotype_label = phenotype_label(phenotype),
  133. phenotype_category = phenotype_category(phenotype),
  134. group1_label = mapply(decode_group, group1_code, phenotype, USE.NAMES = FALSE),
  135. group2_label = mapply(decode_group, group2_code, phenotype, USE.NAMES = FALSE),
  136. comparison_label = ifelse(
  137. !is.na(group1_label) & !is.na(group2_label),
  138. paste0(group1_label, " vs ", group2_label),
  139. NA_character_
  140. ),
  141. # numeric stages (for Braak / CERAD only) to help with ordering
  142. stage1_num = dplyr::case_when(
  143. phenotype == "Braak" ~ suppressWarnings(readr::parse_number(group1_code)),
  144. phenotype == "CERAD" ~ suppressWarnings(readr::parse_number(group1_code)),
  145. TRUE ~ NA_real_
  146. ),
  147. stage2_num = dplyr::case_when(
  148. phenotype == "Braak" ~ suppressWarnings(readr::parse_number(group2_code)),
  149. phenotype == "CERAD" ~ suppressWarnings(readr::parse_number(group2_code)),
  150. TRUE ~ NA_real_
  151. )
  152. ) |>
  153. # nice column order
  154. select(
  155. file,
  156. cohort, cohort_full,
  157. region_code, region,
  158. data_type, data_type_full,
  159. phenotype, phenotype_label, phenotype_category,
  160. comparison, group1_code, group1_label, group2_code, group2_label,
  161. comparison_label, stage1_num, stage2_num
  162. )
  163. }
  165. metadata <- parse_filenames(dge_files)
  166. ```
  169. ```{r}
  170. selected_meta <- metadata |>
  171. filter(
  172. region_code %in% c("PFC", "FP", "IFG", "PHG", "STG"),
  173. phenotype == "CpDxAll",
  174. comparison == "AD-NCI"
  175. ) %>%
  176. group_by(region_code) %>%
  177. # If data_type RC available, prefer that, otherwise take whatever else is there
  178. slice(
  179. if (any(data_type == "RC")) {
  180. which(data_type == "RC")[1]
  181. } else {
  182. 1
  183. }
  184. ) %>%
  185. ungroup()
  187. dge_raw <- selected_meta %>%
  188. select(file, cohort, region_code) %>%
  189. mutate(
  190. data = map(file, \(x) read_csv(file.path(base_dir, x)))
  191. ) %>%
  192. unnest(data) %>%
  193. mutate(
  194. signed_p = -sign(logFC) * log10(PValue)
  195. )
  196. ```
  198. ## FGSEA
  200. IS Genes are from Sudeshna's list
  202. ```{r}
  203. library(fgsea)
  207. is_genes <- c("IFIH1", "DDX58", "IRF1", "IRF7", "CCL2", "MKX", "HSH2D", "SLC1A1",
  208. "LRG1", "SLFN5", "APOL2", "FBXO6", "MAB21L2", "IRF2", "C22orf28",
  209. "GZMB", "TNFAIP6", "PI4K2B", "PBEF1", "CCDC109B", "GK", "IFIT5",
  210. "ARG2", "MAFB", "SIRPA", "UPP2", "EIF2AK2", "LAP3", "GTPBP2",
  211. "BATF2", "GCH1", "SERPINE1", "PUS1", "PFKFB3", "MAP3K14", "BCL2L14",
  212. "ANGPTL1", "PDK1", "LGALS3", "IFNGR1", "SERPING1", "CRP", "IMPA2",
  213. "CYP1B1", "ALDH1A1", "TMEM49", "TNFSF13B", "HPSE", "TYMP", "TRIM14",
  214. "FAM70A", "CXCL9", "G6PC", "IGFBP2", "OASL", "PDGFRL", "CCL19",
  215. "GEM", "XAF1", "HLA-G", "C5orf39", "USP18", "JUNB", "C4orf33",
  216. "SAT3", "TNFSF10", "VEGFC", "CD80", "CCDC92", "C5orf27", "THBD",
  217. "CCL8", "ETV7", "MT1H", "NRN1", "C15orf48", "ADFP", "DDIT4",
  218. "LY6E", "TLR3", "P2RY6", "GBP2", "AGPAT9", "NCF1", "EPSTI1",
  219. "AIM2", "PNRC1", "STEAP4", "LGALS9", "PARP12", "IFI44", "CMAH",
  220. "PRAME", "CEBPD", "SECTM1", "COMMD3", "TMEM51", "PCTK2", "FAM125B",
  221. "BLVRA", "FUT4", "TRIM38", "CREB3L3", "VAMP5", "B2M", "CD69",
  222. "IRF9", "SOCS1", "SOCS2", "DEFB1", "CFB", "IFI35", "APOL1", "SLC25A28",
  223. "WARS", "CCL5", "C10orf10", "IFITM2", "IFIT1", "FLJ39739", "NUP50",
  224. "MYD88", "DYNLT1", "DTX3L", "NAPA", "CSDA", "NOD2", "NT5C3",
  225. "PMM2", "MT1F", "S100A8", "ISG15", "CES1", "IL17RB", "FNDC4",
  226. "MT1M", "NFIL3", "PXK", "ENPP1", "CASP7", "HES4", "IFI30", "EPAS1",
  227. "LGMN", "SSBP3", "PADI2", "PMAIP1", "FLJ23556", "EXT1", "KIAA1618",
  228. "MAX", "SCARB2", "MTHFD2L", "GTPBP1", "CTCFL", "SPTLC2", "IFI44L",
  229. "MT1X", "CCL4", "IL28RA", "CD9", "ZNF313", "FAM46C", "RPL22",
  230. "HLA-E", "NDC80", "RNASE4", "LINCR", "TRIM21", "ADAMDEC1", "HK2",
  231. "IFITM3", "GAK", "SP110", "ANKRD22", "SAA1", "CXCL10", "MT1G",
  232. "C2orf31", "HESX1", "ABTB2", "GMPR", "TNFRSF10A", "EIF3L", "DCP1A",
  233. "SMAD3", "BUB1", "BTN3A3", "KIAA0040", "PML", "MAP3K5", "OAS2",
  234. "TRIM5", "MAFF", "UBE2L6", "RASSF4", "TAP2", "CXCL11", "CDKN1A",
  235. "ZBP1", "CCDC75", "TREX1", "CCND3", "CPT1A", "CCR1", "CD74",
  236. "DHX58", "MSR1", "TDRD7", "HLA-C", "THOC4", "GLRX", "TLK2", "STAP1",
  237. "ATP10D", "SERPINB9", "AMPH", "SCO2", "ADM", "PHF15", "GPX2",
  238. "MX1", "IFI27", "AXUD1", "ANKFY1", "CLEC2B", "IFIT3", "IFI16",
  239. "FNDC3B", "LMO2", "Gluc", "PSCD1", "LIPA", "PRIC285", "IDO1",
  240. "TIMP1", "IFIT2", "RGS1", "ATF3", "HERC6", "GCA", "PIM3", "SAMHD1",
  241. "PNPT1", "SLC25A30", "IFI6", "CD38", "SPSB1", "STAT1", "TMEM140",
  242. "ZNF385B", "APOBEC3A", "TCF7L2", "TAP1", "FCGR1A", "TBX3", "ISG20",
  243. "CHMP5", "LAMP3", "BAG1", "SNN", "IL6ST", "PHF11", "IFI6", "UNC84B",
  244. "GBP5", "PTMA", "RARRES3", "MARCKS", "ETV6", "IL1RN", "PPM1K",
  245. "FKBP5", "GBP1", "MCL1", "FFAR2", "IL1R", "AKT3", "AHNAK2", "NPAS2",
  246. "RNF19B", "HEG1", "CCNA1", "C1S", "NMI", "DDX3X", "CLEC4E", "ODC1",
  247. "JAK2", "PSMB8", "ERLIN1", "GJA4", "GBP3", "IL15", "RIPK2", "ULK4",
  248. "IL15RA", "CD274", "PLSCR1", "MASTL", "C9orf91", "TXNIP", "RTP4",
  249. "ACSL1", "KIAA0082", "C4orf32", "SLC15A3", "MX1", "BCL3", "EHD4",
  250. "DUSP5", "AQP9", "GBP4", "MCOLN2", "PPM1K", "NCOA3", "WHDC1",
  251. "BST2", "CX3CL1", "STARD5", "TRIM34", "TAGAP", "ARNTL", "UNC93B1",
  252. "CLEC4D", "C6orf150", "SAMD4A", "SLC16A1", "FAM134B", "HLA-F",
  253. "FER1L3", "IFITM3", "PRKD2", "PSMB9", "OPTN", "ADAR", "TNFAIP3",
  254. "Fluc", "ABLIM3", "STAT2", "ARHGEF3", "MICB", "RBCK1", "OGFR",
  255. "ELF1", "CRY1", "DNAPTP6", "TRAFD1", "FAM46A", "TRIM25", "GALNT2",
  256. "CD163", "LEPR", "B4GALT5")
  258. library(msigdbr)
  260. kegg_medicus <- msigdbr(
  261. collection = "C2", subcollection = "CP:KEGG_MEDICUS"
  262. )
  264. library(biomaRt)
  266. # Get biomart for human genes
  267. mart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  269. # Map gene symbols to Ensembl IDs
  270. is_gene_mapping <- getBM(
  271. attributes = c("hgnc_symbol", "ensembl_gene_id"),
  272. filters = "hgnc_symbol",
  273. values = is_genes,
  274. mart = mart
  275. )
  277. gene_sets_all <- kegg_medicus %>%
  278. split(.$gs_name) %>%
  279. map("ensembl_gene") %>%
  280. c(list(isg = unique(is_gene_mapping$ensembl_gene_id)))
  282. fgsea_res_raw <- dge_raw %>%
  283. drop_na(GeneSymbol) %>%
  284. group_nest(file) %>%
  285. mutate(
  286. vec = map(
  287. data,
  288. \(x) with(x, set_names(signed_p, EnsemblID))
  289. ),
  290. res = map(
  291. vec,
  292. \(x) fgseaMultilevel(
  293. gene_sets_all,
  294. x,
  295. scoreType = "pos"
  296. # nPermSimple = 10000
  297. )
  298. ),
  299. enr_data = map(
  300. vec,
  301. \(x) plotEnrichmentData(
  302. unique(is_gene_mapping$ensembl_gene_id),
  303. x
  304. )
  305. )
  306. )
  308. fgsea_res <- fgsea_res_raw %>%
  309. dplyr::select(file, res) %>%
  310. unnest(res) %>%
  311. group_by(file) %>%
  312. mutate(
  313. signed_p = -sign(NES) * log10(padj),
  314. rank_signed_p = rank(signed_p),
  315. rank_NES = rank(NES),
  316. pathway_name = str_remove(
  317. pathway,
  318. "KEGG_MEDICUS_[^_]*_"
  319. ) %>%
  320. str_replace_all(fixed("_"), " ")
  321. ) %>%
  322. ungroup() %>%
  323. power_inner_join(
  324. selected_meta,
  325. by = "file",
  326. check = check_specs(
  327. unmatched_keys_left = "warn",
  328. duplicate_keys_right = "warn"
  329. )
  330. )
  331. ```
  334. ### Plot FGSEA results
  336. First plot the position of ISG in the distribution of all gene sets
  338. ```{r}
  339. library(ggrepel)
  341. p <- fgsea_res %>%
  342. ggplot(
  343. aes(
  344. signed_p
  345. )
  346. ) +
  347. geom_density(
  348. aes(
  349. y = after_stat(ndensity)
  350. ),
  351. fill = "gray"
  352. # adjust = .5
  353. ) +
  354. geom_segment(
  355. aes(xend = signed_p),
  356. y = 0, yend = .1,
  357. linewidth = .2
  358. ) +
  359. geom_text_repel(
  360. aes(
  361. label = "ISG"
  362. ),
  363. data = \(x) filter(x, pathway == "isg"),
  364. y = .1,
  365. ylim = c(.8, NA),
  366. color = "red",
  367. fontface = "bold",
  368. angle = 90,
  369. hjust = 1,
  370. vjust = 1,
  371. direction = "y"
  372. ) +
  373. scale_x_continuous(
  374. limits = c(0, NA),
  375. expand = expansion(mult = 0)
  376. ) +
  377. scale_y_continuous(expand = expansion(mult = c(0, .1))) +
  378. coord_cartesian(clip = "off") +
  379. facet_wrap(
  380. vars(region_code),
  381. scales = "free_x"
  382. ) +
  383. labs(
  384. x = "-log10 adjusted p-value",
  385. y = "Density"
  386. ) +
  387. theme(
  388. panel.spacing.x = unit(.3, "cm")
  389. )
  390. # envalysis::theme_publish()
  391. p
  393. ggsave(
  394. file.path("plots", "fgsea_ad_vs_nci_isg_position_signed_p.pdf"),
  395. plot = p,
  396. width = 4,
  397. height = 3
  398. )
  399. ```
  402. ```{r}
  403. library(gt)
  405. fgsea_gt <- fgsea_res %>%
  406. arrange(pval) %>%
  407. transmute(
  408. pathway = str_remove_all(
  409. pathway, "KEGG_MEDICUS_[^_]+_"
  410. ) %>%
  411. str_replace_all(fixed("_"), " ") %>%
  412. recode(isg = "ISG"),
  413. region_code,
  414. padj, NES
  415. ) %>%
  416. head(n = 15) %>%
  417. gt() %>%
  418. fmt_number(
  419. columns = c(NES),
  420. n_sigfig = 2
  421. ) %>%
  422. fmt_scientific(
  423. columns = c(padj),
  424. n_sigfig = 2
  425. ) %>%
  426. tab_style(
  427. style = cell_text(color = "red", weight = "bold"),
  428. locations = cells_body(
  429. columns = pathway,
  430. rows = pathway == "ISG"
  431. )
  432. ) %>%
  433. opt_table_font(
  434. font = "Helvetica"
  435. )
  437. gtsave(
  438. fgsea_gt,
  439. file.path("plots", "fgsea_ad_vs_nci_table.html")
  440. )
  442. ```
  445. ```{r}
  446. fgsea_plot_data <- fgsea_res_raw %>%
  447. dplyr::select(file, enr_data) %>%
  448. unnest_wider(enr_data) %>%
  449. summarize(
  450. across(
  451. where(is.list),
  452. \(x) set_names(x, file) |>
  453. bind_rows(.id = "file") |>
  454. list()
  455. ),
  456. marks = pick(
  457. where(negate(is.list))
  458. ) %>%
  459. list()
  460. )
  462. # Create binned data for ribbon
  463. stats_data <- fgsea_plot_data$stats[[1]]
  464. n_bins <- 100
  465. stat_range <- range(stats_data$stat, na.rm = TRUE)
  466. bin_breaks <- seq(stat_range[1], stat_range[2], length.out = n_bins + 1)
  468. binned_stats <- stats_data %>%
  469. mutate(
  470. bin = cut(stat, breaks = bin_breaks, include.lowest = TRUE, labels = FALSE)
  471. ) %>%
  472. group_by(file, bin) %>%
  473. summarise(
  474. xmin = min(rank),
  475. xmax = max(rank),
  476. avg_stat = mean(stat, na.rm = TRUE),
  477. .groups = "drop"
  478. )
  480. fgsea_plot <- ggplot(
  481. fgsea_plot_data$curve[[1]],
  482. aes(
  483. rank, ES
  484. )
  485. ) +
  486. geom_hline(yintercept = 0, linetype = "dashed", color = "gray", linewidth = 1) +
  487. geom_line() +
  488. geom_segment(
  489. aes(x = rank, xend = rank),
  490. data = fgsea_plot_data$ticks[[1]],
  491. y = -.35, yend = -.25,
  492. linewidth = .25
  493. ) +
  494. geom_rect(
  495. aes(xmin = xmin, xmax = xmax, fill = avg_stat),
  496. data = binned_stats,
  497. ymin = -0.3, ymax = -0.35,
  498. alpha = .95,
  499. inherit.aes = FALSE
  500. ) +
  501. scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
  502. facet_wrap(~file) +
  503. lims(
  504. y = c(-0.32, 0.55)
  505. ) +
  506. labs(
  507. x = "Expression rank",
  508. y = "Enrichment Score",
  509. fill = "Differential expression\nAD vs NCI"
  510. )
  512. fgsea_plot
  514. ggsave(
  515. file.path("plots", "fgsea_ad_vs_nci_isg.pdf"),
  516. plot = fgsea_plot,
  517. width = 10,
  518. height = 8
  519. )
  520. ```

ad_vs_healthy_gsea.Rmd at commit 4d97ea4, under MIT · at the source

Overview

Authors: Laura E. König1,2, Steve Rodriguez1,2, Clemens Hug1, Shayda Daneshvari1,2, Alexander Chung1,2, Mark Appleman1,2, Max Tsai1,2, Gary A. Bradshaw1, Asli Sahin2, Yuyu Song1,2, George Zhou2, Robyn J. Eisert1,3, Federica Piccioni4,5, Christine Marques2,6, Sharon Powley2, James Yarmolinsky7,8, Brian J. Wainger2, Sudeshna Das2,6, Marian Kalocsay1,9, Abbas Dehghan7,8, Ioanna Tzoulaki7,8,10, Artem Sokolov1,11, Peter Sorger1, David E. Root4, Mark W. Albers1,2
ORCID iDs: Laura E. König, Clemens Hug, Alexander Chung, Mark Appleman, Yuyu Song, George Zhou, Robyn J. Eisert, Federica Piccioni, Sharon Powley, Brian J. Wainger, Sudeshna Das, Marian Kalocsay, Abbas Dehghan, Ioanna Tzoulaki, Artem Sokolov, Peter Sorger, David E. Root, Mark W. Albers
  1. Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School,Boston, 02115 MA USA
  2. Department of Neurology, Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital,Charlestown, MA USA
  3. Present Address: The Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School,Boston, MA USA
  4. Broad Institute of MIT and Harvard,Cambridge, MA USA
  5. Present Address: Merck Research Laboratories,Cambridge, MA USA
  6. Harvard Medical School,Boston, MA USA
  7. Dementia Research Institute, Imperial College London,London, UK
  8. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London,London, UK
  9. Present Address: Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center,Houston, TX USA
  10. Biomedical Research Foundation, Academy of Athens,Athens, Greece
  11. Present Address: Etiome, Cambridge, MA USA
Institutions: Harvard University (United States); Massachusetts General Hospital (United States); Broad Institute (United States); Present Address: Merck Research Laboratories,Cambridge, MA USA; Imperial College London (United Kingdom); The University of Texas MD Anderson Cancer Center (United States); Academy of Athens (Greece)
Journal: Nature communications, volume 17, issue 1, article 3967
Dates: received 20 September 2024; accepted 23 February 2026; published online 14 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70243-3 · PMID 41832177 · PMCID PMC13133158 · OpenAlex W7135390020
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Alzheimer's disease
MeSH: Alzheimer Disease*, Brain*, DNA-Binding Proteins*, Neuroinflammatory Diseases*, TYK2 Kinase*, Animals, Female, Humans, Male, Neurons, Pyrazoles, Pyrimidines (* major topic)
Topic: RNA regulation and disease (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH (R01 AG058063, R01 AG078297); Coins for Alzheimer's Research Trust (CART Fund); Target ALS (Target ALS Foundation)
Citations: cited by 1 paper (Europe PMC); 123 references in the paper

Abstract

Neuroinflammation is a pathological feature of neurodegenerative diseases like Alzheimer’s disease and ALS. Cytoplasmic dsRNA (cdsRNA) triggers a type-I interferon response in human neural cells, leading to their death, and is found in neurons of C9ORF72-ALS patients. Here, we report the spatial coincidence of cdsRNA and pTDP-43 inclusions in human postmortem tissue with Alzheimer’s disease pathology, and upregulated interferon response genes in affected regions. CdsRNA also accumulates in a human TDP-43 G298S iPSC cortical neuronal model. We use cryptic exon detection as a proxy for TDP-43 mislocalization and demonstrate that FDA-approved JAK inhibitors baricitinib and ruxolitinib, which block interferon signaling, show protective effects only in brains with elevated cryptic exon expression. A CRISPR screen reveals TYK2 as a top hit, and TYK2 knockdown and the selective TYK2 inhibitor deucravacitinib rescue cdsRNA-induced toxicity. We find parallel neuroinflammatory mechanisms, dependent on TYK2 - a potential disease-modifying target - for TDP-43-associated Alzheimer’s disease and C9ORF72-ALS.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

Zenodo 18100691

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (14 files), data.table (11 files), broom (2 files), circlize (2 files), ComplexHeatmap (2 files), Salmon (2 files), ggpubr (1 file), Plotly (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
20 files
At the source:

labsyspharm/ad-personalized

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4d97ea4c555af89206798c50de057950306f736a, 29 May 2026
Languages: R (13), Shell (5)
Size: 20 files, 18 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, 5 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (14 files), data.table (11 files), broom (2 files), circlize (2 files), ComplexHeatmap (2 files), Salmon (2 files), ggpubr (1 file), Plotly (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
20 files
At the source: github.com/labsyspharm/ad-personalized/tree/20251230

Code availability

Scripts to fully reproduce the tables and figures represented in this manuscript are provided on GitHub (10.5281/zenodo.18100691)122.

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 36 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

Datasets cited

Data availability

For all compounds with an HMS LINCS ID, the compound information, including the vendors they were purchased from, can be found on the HMS LINCS website (https://lincs.hms.harvard.edu/). The proteomics raw data and search results generated in this study have been deposited in the ProteomeXchange Consortium via the PRIDE121 partner repository under accession code PXD043641 (https://www.ebi.ac.uk/pride/archive/projects/PXD043641). A reporting summary for this article is available as Supplementary Information file. Source Data are provided with this paper. The source data underlying Figs. 1c, d, 4d-h, 5, 6a,b, 7b,c, Supplementary Figs. 1c, 2b, 8a–f, h, and 9 are provided in the Source Data file. The bulk RNA-sequencing data used in this study are available in the ROSMAP32 and MSBB35 databases obtained from the AMP-AD Knowledge Portal under the accession code syn2580853 on Synapse [10.7303/syn2580853]. Source data are provided with this paper.

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

Versions

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

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 1 keyword, 12 MeSH terms, 3 funders, 122 references.

Cite

This paper

König, L. E., Rodriguez, S., Hug, C., Daneshvari, S., Chung, A., Appleman, M., Tsai, M., Bradshaw, G. A., Sahin, A., Song, Y., Zhou, G., Eisert, R. J., Piccioni, F., Marques, C., Powley, S., Yarmolinsky, J., Wainger, B. J., Das, S., Kalocsay, M., . . . Albers, M. W. (2026). TYK2 mediates neuroinflammation in Alzheimer's disease brains with TDP-43 pathology. Nature communications, 17(1), 3967. https://doi.org/10.1038/s41467-026-70243-3

BibTeX

@article{konig2026tyk2,
author = {König, Laura E. and Rodriguez, Steve and Hug, Clemens and Daneshvari, Shayda and Chung, Alexander and Appleman, Mark and Tsai, Max and Bradshaw, Gary A. and Sahin, Asli and Song, Yuyu and Zhou, George and Eisert, Robyn J. and Piccioni, Federica and Marques, Christine and Powley, Sharon and Yarmolinsky, James and Wainger, Brian J. and Das, Sudeshna and Kalocsay, Marian and Dehghan, Abbas and Tzoulaki, Ioanna and Sokolov, Artem and Sorger, Peter and Root, David E. and Albers, Mark W.},
title = {{TYK2 mediates neuroinflammation in Alzheimer's disease brains with TDP-43 pathology}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3967},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70243-3},
url = {https://doi.org/10.1038/s41467-026-70243-3},
pmid = {41832177},
pmcid = {PMC13133158}
}

RIS

TY - JOUR
AU - König, Laura E.
AU - Rodriguez, Steve
AU - Hug, Clemens
AU - Daneshvari, Shayda
AU - Chung, Alexander
AU - Appleman, Mark
AU - Tsai, Max
AU - Bradshaw, Gary A.
AU - Sahin, Asli
AU - Song, Yuyu
AU - Zhou, George
AU - Eisert, Robyn J.
AU - Piccioni, Federica
AU - Marques, Christine
AU - Powley, Sharon
AU - Yarmolinsky, James
AU - Wainger, Brian J.
AU - Das, Sudeshna
AU - Kalocsay, Marian
AU - Dehghan, Abbas
AU - Tzoulaki, Ioanna
AU - Sokolov, Artem
AU - Sorger, Peter
AU - Root, David E.
AU - Albers, Mark W.
TI - TYK2 mediates neuroinflammation in Alzheimer's disease brains with TDP-43 pathology
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/14
VL - 17
IS - 1
SP - 3967
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70243-3
UR - https://doi.org/10.1038/s41467-026-70243-3
LA - en
ER -

CSL-JSON

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