OSCR

Microcephaly-like phenotype triggered by novel reassortant and prototypic Oropouche virus strains in brain organoids.

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  1. [1] § Results › Proteomic profiling reveals coordinated suppression of translation, ECM and neuroglial programs upon OROV infection ↔ 4.Proteomics.Rmd, lines 215–345 · score 0.97 · OROV_L, OROV_M, OROV_NSs, POLR2G, COL18A1, COL4A5
  2. [2] § Results › Proteomic profiling reveals coordinated suppression of translation, ECM and neuroglial programs upon OROV infection ↔ 4.Proteomics.Rmd, lines 101–213 · score 0.93 · chemokine production, cytoplasmic translation, canonical Wnt, long term synaptic, synaptic transmission, positive regulation
  3. [3] § Results › OROV infects human NSCs and modulates neurodevelopmental markers ↔ 3.Volcanoplots_wmarkers.Rmd, lines 84–152 · score 0.90 · APOBEC3A, CYP26A1, COL12A1, COL1A1, COL4A4, IL18
  4. [4] § Results › OROV infects human NSCs and modulates neurodevelopmental markers ↔ 2.Lolipop_pathways.Rmd, lines 42–69 · score 0.85 · host cell, neuron projection development, viral release, viral gene, negative regulation, gene expression
  5. [5] § Results › Proteomic profiling reveals coordinated suppression of translation, ECM and neuroglial programs upon OROV infection ↔ 4.Proteomics.Rmd, lines 215–345 · score 0.76 · COL18A1, COL6A1, ITGA2, LAMB1, SULF2, heatmap
  6. [6] § Results › OROV infects human NSCs and modulates neurodevelopmental markers ↔ 1.Import_deseq2_firstanalyses.Rmd, lines 700–743 · score 0.55 · intrinsic apoptotic, negative regulation, RNA, neural, neuron, pathways

Paper

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The authors' code

R Markdown · 560 lines · 16 KB · GPL-3.0 · 3 matches

  1. ---
  2. title: "Proteomics"
  3. author: "Izabela Mamede"
  4. date: "2025-11-12"
  5. output: html_document
  6. ---
  8. ```{r}
  9. library(tidyomics)
  10. library(ggplot2)
  11. library(fgsea)
  12. ```
  14. load all
  15. ```{r}
  16. infile <- "data-raw/amica_protein_groups_all.xlsx" # ajuste o caminho se preciso
  18. raw_prot<-read_xlsx(infile) %>% clean_names()
  20. # --- Pacotes
  21. suppressPackageStartupMessages({
  22. library(dplyr); library(tidyr); library(stringr); library(purrr); library(janitor)
  23. })
  25. # raw_prot já lido; se precisar:
  26. # raw_prot <- readxl::read_xlsx("amica_protein_groups_all.xlsx", sheet = "amica_protein_groups") |> janitor::clean_names()
  28. # --- Flags de presença de colunas (evita usar '.' dentro do mutate)
  29. has_gene_names <- "gene_names" %in% names(raw_prot)
  30. has_majority_prot_ids <- "majority_protein_i_ds" %in% names(raw_prot)
  31. has_protein_names <- "protein_names" %in% names(raw_prot)
  33. # --- 0) Chaves estáveis: gene_symbol + protein_id
  34. rp <- raw_prot |>
  35. mutate(
  36. gene_symbol = if (has_gene_names) sub(";.*$", "", gene_names) else NA_character_,
  37. gene_symbol = trimws(as.character(gene_symbol)),
  38. protein_id = dplyr::case_when(
  39. has_majority_prot_ids ~ as.character(majority_protein_i_ds),
  40. has_protein_names ~ as.character(protein_names),
  41. TRUE ~ NA_character_
  42. )
  43. ) |>
  44. filter(!is.na(gene_symbol), gene_symbol != "")
  46. # --- 1) LONG único parseando stat + grupos a partir do nome da coluna (snake_case)
  47. long_all <- rp |>
  48. select(
  49. gene_symbol, protein_id,
  50. matches("^(log_fc|adj_p_val|p_value)_(orov|be_an)_vs_(mock|be_an)$")
  51. ) |>
  52. pivot_longer(
  53. cols = matches("^(log_fc|adj_p_val|p_value)_(orov|be_an)_vs_(mock|be_an)$"),
  54. names_to = c("stat","g1","g2"),
  55. names_pattern = "^(log_fc|adj_p_val|p_value)_(orov|be_an)_vs_(mock|be_an)$",
  56. values_to = "value",
  57. values_transform = list(value = as.numeric)
  58. ) |>
  59. mutate(
  60. comparison = dplyr::case_when(
  61. g1 == "be_an" & g2 == "mock" ~ "BE_vs_Ctrl",
  62. g1 == "orov" & g2 == "mock" ~ "RJ_vs_Ctrl",
  63. g1 == "orov" & g2 == "be_an" ~ "RJ_vs_BE",
  64. TRUE ~ paste0(g1,"_vs_",g2)
  65. ),
  66. stat = dplyr::recode(stat,
  67. "log_fc" = "logfc",
  68. "adj_p_val" = "adjpvalue",
  69. "p_value" = "pvalue")
  70. ) |>
  71. filter(!is.na(comparison))
  73. # --- 2) Colapsar duplicatas exatas por (gene, protein, comparison, stat)
  74. # (sem usar first(); pegamos o primeiro não-NA na mão)
  75. long_collapsed <- long_all |>
  76. group_by(gene_symbol, protein_id, comparison, stat) |>
  77. summarise(
  78. value = {
  79. v <- value[!is.na(value)]
  80. if (length(v) == 0L) NA_real_ else v[1]
  81. },
  82. .groups = "drop"
  83. )
  85. # --- 3) WIDE mantendo variantes por proteína e rotulando como GENE_1, GENE_2, ...
  86. dat_prot_variants <- long_collapsed |>
  87. pivot_wider(
  88. id_cols = c(gene_symbol, protein_id, comparison),
  89. names_from = stat,
  90. values_from = value,
  91. values_fill = NA_real_
  92. ) |>
  93. arrange(gene_symbol, protein_id, comparison) |>
  94. group_by(gene_symbol) |>
  95. mutate(variant_symbol = paste0(gene_symbol, "_", dense_rank(factor(protein_id)))) |>
  96. ungroup() |>
  97. mutate(comparison = factor(comparison, levels = c("BE_vs_Ctrl","RJ_vs_Ctrl","RJ_vs_BE")))
  99. ```
  101. Plotting
  103. ```{r}
  104. suppressPackageStartupMessages({
  105. library(dplyr); library(tidyr); library(stringr)
  106. library(msigdbr); library(fgsea); library(ggplot2)
  107. })
  110. gene_level <- dat_prot_variants %>%
  111. group_by(comparison, gene_symbol) %>%
  112. summarise(
  113. logfc_gene = {
  114. v <- logfc[!is.na(logfc)]
  115. if (length(v) == 0L) NA_real_ else v[which.max(abs(v))][1]
  116. },
  117. .groups = "drop"
  118. ) %>%
  119. filter(!is.na(logfc_gene)) %>%
  120. mutate(comparison = factor(comparison, levels = c("BE_vs_Ctrl","RJ_vs_Ctrl","RJ_vs_BE")))
  122. # ------------------------------------------------------------------
  123. # 2) Gene sets GO:BP (msigdbr)
  125. # --- FGSEA only GO:BP (multilevel), no KEGG ---
  127. suppressPackageStartupMessages({
  128. library(dplyr); library(msigdbr); library(fgsea); library(tidyr); library(stringr); library(ggplot2)
  129. })
  133. # 1) Gene sets GO:BP
  134. msig_go <- msigdbr(species = "Homo sapiens", category = "C5", subcategory = "GO:BP") %>%
  135. dplyr::select(gs_name, gene_symbol)
  136. gmt_go <- split(msig_go$gene_symbol, msig_go$gs_name)
  138. # 2) Multilevel FGSEA helper
  139. run_fgsea_gobp <- function(df_comp) {
  140. ranks <- setNames(df_comp$logfc_gene, df_comp$gene_symbol)
  141. ranks <- ranks[!is.na(ranks)]
  142. if (any(duplicated(names(ranks)))) {
  143. ranks <- tapply(ranks, names(ranks), function(v) v[which.max(abs(v))])
  144. }
  145. fgsea(pathways = gmt_go, stats = ranks, minSize = 10, maxSize = 2000) %>%
  146. tibble::as_tibble()
  147. }
  149. # 3) Run per comparison
  150. fgsea_go <- gene_level %>%
  151. dplyr::group_by(comparison) %>%
  152. dplyr::group_modify(~ run_fgsea_gobp(.x)) %>%
  153. dplyr::ungroup() %>%
  154. dplyr::rename(pathway = pathway, NES = NES, padj = padj) %>%
  155. dplyr::mutate(comparisson = factor(comparison, levels = c("BE_vs_Ctrl","RJ_vs_Ctrl","RJ_vs_BE")))
  157. selected_paths<-c(
  158. "GOBP_RESPONSE_TO_TYPE_I_INTERFERON",
  159. "GOBP_WNT_SIGNALING_PATHWAY",
  160. "GOBP_LONG_TERM_SYNAPTIC_POTENTIATION",
  161. "GOBP_RESPONSE_TO_CALCIUM_ION",
  162. "GOBP_CANONICAL_WNT_SIGNALING_PATHWAY",
  163. "GOBP_CHEMOKINE_PRODUCTION",
  164. "GOBP_POSITIVE_REGULATION_OF_SYNAPTIC_TRANSMISSION",
  165. "GOBP_CYTOPLASMIC_TRANSLATION",
  166. "GOBP_TRANSLATION",
  167. "GOBP_RIBOSOME_ASSEMBLY"
  168. )
  172. # dotplot right after (same palette):
  173. selected_paths <- fgsea_go %>%
  174. dplyr::filter(pval < 0.05) %>%
  175. dplyr::filter(pathway %in% selected_paths
  176. ) %>%
  177. dplyr::arrange(padj) %>%
  178. dplyr::pull(pathway) %>%
  179. unique()
  181. #saving if need
  182. #data.table::fwrite(selected_paths, file="fgsea.tsv", sep="\t", sep2=c("", " ", ""))
  184. p_dot_gobp <- fgsea_go %>%
  185. dplyr::filter(pathway %in% selected_paths) %>%
  186. ggplot(aes(x = comparisson, y = pathway)) +
  187. geom_point(aes(size = -log(padj), color = NES)) +
  188. scale_color_gradient2(high = "coral", low = "#2AB7CA", mid = "white") +
  189. scale_size_continuous(range = c(2, 7)) +
  190. theme_bw() +
  191. scale_x_discrete(guide = guide_axis(angle = 45)) +
  192. labs(x = NULL, y = NULL, color = "NES", size = "-log(padj)")
  194. #saving if need
  195. # ggsave("Fig5A_GO_BP_only_dotplot.pdf", p_dot_gobp, width = 8, height = 6)
  198. ggsave("Fig5A_GO_BP_only_dotplot_sel.pdf", p_dot_gobp, width = 7, height = 4)
  200. # without RJ_vs_BE
  201. p_dot_noRJBE <- fgsea_go %>%
  202. filter(pathway %in% selected_paths,
  203. comparisson != "RJ_vs_BE") %>%
  204. ggplot(aes(x = comparisson, y = pathway)) +
  205. geom_point(aes(size = -log(padj), color = NES)) +
  206. scale_color_gradient2(high = "coral", low = "#2AB7CA", mid = "white") +
  207. scale_size_continuous(range = c(2, 7)) +
  208. theme_bw() +
  209. scale_x_discrete(guide = guide_axis(angle = 45)) +
  210. labs(x = NULL, y = NULL, color = "NES", size = "-log(padj)")
  212. ggsave("Fig5A_GO_BP_only_dotplot_noRJvsBE_sel.pdf", p_dot_noRJBE, width = 7, height = 4)
  213. ```
  215. ```{r}
  216. ########################
  217. # volcano plot
  219. genes_to_mark<- c( "OROV_M", "OROV_NSs", "OROV_L", "OROV_N", "COL6A1",
  220. "COL18A1", "COL4A6", "COL4A5", "COL2A1", "EMX2",
  221. "POLR2G", "RPL39", "LGALS7", "LGALS1", "LAMB1", "ITGA2",
  222. "SLIT2", "SULF2")
  225. RJ_vs_Ctrl_only<-dat_prot_aug %>%
  226. filter(comparison == "RJ_vs_Ctrl")
  229. RJ_vs_Ctrl_only %>%
  230. EnhancedVolcano(lab = RJ_vs_Ctrl_only$gene_symbol,
  231. x = 'logfc',
  232. y = 'pvalue',
  233. FCcutoff = 1,
  234. selectLab = genes_to_mark,
  235. col = c('grey', 'grey', 'grey', 'salmon'),
  236. drawConnectors = TRUE,
  237. ylab = "p value",
  238. title = "RJ_vs_Mock",
  239. pCutoff = 0.05,
  240. subtitle = NULL,
  241. labSize = 3,
  242. titleLabSize = 18)
  244. cowplot::ggsave2("Plots/Volc_Prot_RJ_vs_Mock.pdf", height = 6, width = 7)
  247. Be_vs_Ctrl_only<-dat_prot_aug %>%
  248. filter(comparison == "BE_vs_Ctrl")
  251. Be_vs_Ctrl_only %>%
  252. EnhancedVolcano(lab = Be_vs_Ctrl_only$gene_symbol,
  253. x = 'logfc',
  254. y = 'pvalue',
  255. FCcutoff = 1,
  256. selectLab = genes_to_mark,
  257. col = c('grey', 'grey', 'grey', 'salmon'),
  258. drawConnectors = TRUE,
  259. ylab = "p value",
  260. title = "BeAn_vs_Mock",
  261. pCutoff = 0.05,
  262. subtitle = NULL,
  263. labSize = 3,
  264. titleLabSize = 18)
  266. cowplot::ggsave2("Plots/Volc_Prot_BeAn_vs_Mock.pdf", height = 6, width = 7)
  269. #markers by category
  271. markers_tbl <- tribble(
  272. ~Category, ~Genes,
  273. "Neurons", "ACOT7, APBB1, APP, CADM3, CELF3, CHN1, CPE, DCLK2, ELAVL3, KIF1A, KIF1B, KIF5A, LRRN1, PFN2, PLCH1, SEZ6L2, TUBA1A, TUBB2A, UNC119",
  274. "Astrocytes", "NCAM",
  275. "NSC", "EMX2, MSI1, OTX2, DCHS1, NDE1",
  276. "ECM / Adhesion", "COL6A1, COL18A1, LAMB1, ITGA2, SLIT2, SULF2"
  277. ) %>%
  278. separate_rows(Genes, sep = ",\\s*") %>%
  279. rename(gene_symbol = Genes) %>%
  280. mutate(gene_symbol = trimws(gene_symbol))
  282. panelD_df <- dat_prot_aug%>%
  283. filter(comparison %in% c("BE_vs_Ctrl","RJ_vs_Ctrl")) %>%
  284. inner_join(markers_tbl, by = "gene_symbol") %>%
  285. mutate(
  286. neg_log10_p = -log2(pmax(adjpvalue, 1e-300)),
  287. comparison = factor(comparison, levels = c("BE_vs_Ctrl","RJ_vs_Ctrl"))
  288. )
  291. panelD_df %>%
  292. ggplot(aes(x = comparison, y = gene_symbol)) +
  293. geom_point(aes(size = neg_log10_p, color = logfc)) +
  294. scale_color_gradient2(low = "#2AB7CA", mid = "white", high = "coral") +
  295. scale_size_continuous(range = c(2, 8)) +
  296. facet_grid(Category ~ ., scales = "free_y", space = "free_y") +
  297. theme_bw() +
  298. theme(
  299. strip.background = element_rect(fill = "grey95"),
  300. axis.text.x = element_text(angle = 45, hjust = 1)
  301. ) +
  302. labs(x = NULL, y = NULL, color = "logFC", size = "-log10(adjp)")
  306. cowplot::ggsave2("Plots/Fig5D_markers_bubble.pdf",width = 5, height = 8)
  308. panelD_df %>%
  309. ggplot(aes(x = comparison, y = gene_symbol)) +
  310. geom_tile(aes(fill = logfc), color = "grey8") +
  311. scale_fill_gradient2(low = "#2AB7CA", mid = "white", high = "coral") +
  312. facet_grid(.~Category, scales = "free", space = "free") +
  313. theme_bw() +
  314. theme(
  315. strip.background = element_rect(fill = "grey95"),
  316. axis.text.x = element_text(angle = 45, hjust = 1)
  317. ) +
  318. coord_flip()
  320. cowplot::ggsave2("Plots/5DHeatmap_markers.pdf",width = 10, height = 2)
  323. panelD_df %>%
  324. ggplot(aes(x = comparison, y = gene_symbol)) +
  325. geom_tile(aes(fill = logfc), color = "grey8") +
  326. scale_fill_gradient2(low = "#2AB7CA", mid = "white", high = "coral") +
  327. facet_grid(Category~., scales = "free", space = "free") +
  328. theme_bw() +
  329. theme(
  330. strip.background = element_rect(fill = "grey95"),
  331. axis.text.x = element_text(angle = 45, hjust = 1)
  332. )
  334. cowplot::ggsave2("Plots/5DHeatmap_markers_empe.pdf",width = 3, height = 10)
  336. #side by side
  337. plot_grid(p_BE, p_RJ, labels = c("B","C"), ncol = 2) %>%
  338. ggsave("Fig5B_C_volcanos_side_by_side.pdf", width = 12.5, height = 5.5)
  341. dat_prot_aug_test<- dat_prot_aug %>% filter(gene_symbol %in% astro_markers & pvalue < 0.05)
  343. dat_prot_aug_test
  345. ```
  347. The second proteomics data
  349. ```{r}
  350. raw_prot$lfq_intensity_m01
  352. mock_intensities<-raw_prot %>% select(gene_names, protein_names, lfq_intensity_m01,
  353. lfq_intensity_m02, lfq_intensity_m03)
  355. mock_intensities$lfq_intensity_m01<-as.numeric(mock_intensities$lfq_intensity_m01)
  357. mock_intensities$lfq_intensity_m02<-as.numeric(mock_intensities$lfq_intensity_m02)
  359. mock_intensities$lfq_intensity_m03<-as.numeric(mock_intensities$lfq_intensity_m03)
  361. mock_intensities_clean <- na.omit(mock_intensities) # no cols=, this checks all columns
  363. ```
  365. Read the 30 and the 60 days
  366. ```{r}
  367. Day_30_org<-read_excel("data-raw/30 days for comparison.xlsx", sheet = 2)
  368. Day_60_org<-read_excel("data-raw/60 days for comparison.xlsx")
  370. # 1) Your genes (HGNC symbols)
  371. genes <- Day_30_org$`30 days` # <<< troca pelos seus
  373. # 2) Choose databases
  374. dbs <- c(
  375. "GO_Biological_Process_2023"
  376. )
  378. # 3) Run Enrichr (single call, no loop, no function)
  379. enr_list <- enrichr(genes, dbs)
  381. genes <- your_gene_vector # e.g. c("TP53", "MYC", "EGFR")
  383. ## Convert SYMBOL -> ENTREZ
  384. gene_df <- bitr(
  385. genes,
  386. fromType = "SYMBOL",
  387. toType = "ENTREZID",
  388. OrgDb = org.Hs.eg.db
  389. )
  391. entrez_genes <- unique(gene_df$ENTREZID)
  393. ## GO Biological Process
  394. ego_bp <- enrichGO(
  395. gene = entrez_genes,
  396. OrgDb = org.Hs.eg.db,
  397. keyType = "ENTREZID",
  398. ont = "BP",
  399. pAdjustMethod = "BH",
  400. pvalueCutoff = 0.05,
  401. qvalueCutoff = 0.2,
  402. readable = TRUE
  403. )
  405. head(go_bp_sig)
  406. neuron_markers <- c(
  407. # core / cytoskeleton
  408. "RBFOX3","MAP2","TUBB3","NEFL","NEFM","NEFH","INA","GAP43",
  409. # synaptic machinery
  410. "SYN1","SYN2","SYN3","SYP","SYT1","STX1A","STX1B","STXBP1","VAMP2",
  411. "DLG1","DLG2","DLG3","DLG4","SHANK1","SHANK2","SHANK3","HOMER1",
  412. "NRXN1","NRXN2","NRXN3","NLGN1","NLGN2","NLGN3","CNTNAP2",
  413. # neurotransmission / transporters
  414. "SLC17A7","SLC17A6","SLC32A1","SLC6A1","SLC6A3","SLC6A4","SLC6A5",
  415. # receptors & channels (glutamate / GABA / ion channels)
  416. "GRIA1","GRIA2","GRIA3","GRIA4","GRIN1","GRIN2A","GRIN2B","GRIK1","GRIK2","GRIK3","GRIK4","GRIK5",
  417. "GABRA1","GABRA2","GABRA3","GABRB2","GABRB3","GABRG2",
  418. "SCN1A","SCN2A","SCN8A","KCNQ2","KCNQ3","KCNB1","KCNC1",
  419. "CACNA1A","CACNA1B","CACNA1C","CACNA1E",
  420. # axon guidance / adhesion
  421. "ROBO2","CNTN2","NCAM1","L1CAM","SEMA3A","PLXNA4",
  422. # neuronal TFs / development
  423. "NEUROD1","NEUROD2","ASCL1","DCX","TBR1","EOMES","FOXG1","PAX6","PROX1",
  424. # subtype/examples
  425. "TH","CHAT","RELN","CAMK2A","CAMK2B","CAMK2G","SNAP25","VAMP2","SLC12A5","BDNF","NTRK2"
  426. )
  427. neuron_markers <- unique(neuron_markers)
  429. # Expanded astrocyte markers (canonical + transporters/metabolism + channels + TFs)
  430. astro_markers <- c(
  431. # canonical
  432. "GFAP","AQP4","S100B","ALDH1L1","ALDOC","FABP7","VIM","GJA1","GLUL","CLU","FGFR3",
  433. "SPARCL1","SPARC","SERPINA3","ITGA6","ITGB4","NDRG2","CST3","CP",
  434. # transporters / neurotransmitter handling
  435. "SLC1A2","SLC1A3","SLC6A11","SLC6A9","SLC38A1","SLC38A3","SLC38A5","SLC13A3",
  436. # pumps / channels / ionic homeostasis
  437. "ATP1A2","ATP1B2","KCNJ10","KCNJ16","KCNJ2","AQP4","MLC1",
  438. # lipid & Apo
  439. "APOE","ABCA1","ABCA7","LPL",
  440. # cytokine/GPCR often enriched in astro
  441. "GPR37L1","GPRC5B","PTGDS","SOCS3","STAT3",
  442. # TFs & gliogenesis
  443. "SOX9","HES5","NFIA","NFIB",
  444. # ECM / structural
  445. "LAMA2","TNC","VCAN"
  446. )
  450. Day_30_org$`30 days`
  452. Day_60_org$`60 days`
  454. mock_intensities_clean$gene_names
  456. ```
  457. EnrichR
  458. ```{r}
  460. ## Packages
  461. library(readxl)
  462. library(dplyr)
  463. library(clusterProfiler)
  464. library(org.Hs.eg.db)
  466. #unique genes
  467. genes_30 <- Day_30_org$`30 days` |> unique() |> na.omit()
  468. genes_60 <- Day_60_org$`60 days` |> unique() |> na.omit()
  470. #symbol to entrez
  471. genes_30_df <- bitr(
  472. genes_30,
  473. fromType = "SYMBOL",
  474. toType = "ENTREZID",
  475. OrgDb = org.Hs.eg.db
  476. )
  478. entrez_30 <- unique(genes_30_df$ENTREZID)
  481. genes_60_df <- bitr(
  482. genes_60,
  483. fromType = "SYMBOL",
  484. toType = "ENTREZID",
  485. OrgDb = org.Hs.eg.db
  486. )
  488. entrez_60 <- unique(genes_60_df$ENTREZID)
  490. #GO BP for both
  491. ego_30 <- enrichGO(
  492. gene = entrez_30,
  493. OrgDb = org.Hs.eg.db,
  494. keyType = "ENTREZID",
  495. ont = "BP",
  496. pAdjustMethod = "BH",
  497. pvalueCutoff = 0.05,
  498. qvalueCutoff = 0.2,
  499. readable = TRUE
  500. )
  502. go_30_res <- as.data.frame(ego_30)
  504. ego_60 <- enrichGO(
  505. gene = entrez_60,
  506. OrgDb = org.Hs.eg.db,
  507. keyType = "ENTREZID",
  508. ont = "BP",
  509. pAdjustMethod = "BH",
  510. pvalueCutoff = 0.05,
  511. qvalueCutoff = 0.2,
  512. readable = TRUE
  513. )
  515. go_60_res <- as.data.frame(ego_60)
  517. write_xlsx(go_30_res,"EnrichR_GOBP_30days.xlsx")
  518. write_xlsx(go_60_res,"EnrichR_GOBP_60days.xlsx")
  520. #check neuron/astrocyte terms
  521. neuron_30 <- go_30_res[grepl("neuron", go_30_res$Description, ignore.case = TRUE), ]
  522. astro_30 <- go_30_res[grepl("astrocyte|glial", go_30_res$Description, ignore.case = TRUE), ]
  524. neuron_30<-neuron_30 %>% mutate(class = "30 days")
  525. neuron_60<-neuron_60 %>% mutate(class = "60 days")
  527. Thirty_and_sixty <-rbind(neuron_30, neuron_60)
  529. Thirty_and_sixty %>%
  530. ggplot(aes(x = class, y = Description)) +
  531. geom_point(aes(size = -log(p.adjust), fill = -log(p.adjust)),
  532. color = "black",shape = 21) +
  533. scale_size_continuous(range = c(2, 7)) +
  534. theme_bw() +
  535. scale_fill_gradient2(low = "white", high = "coral")+
  536. scale_x_discrete(guide = guide_axis(angle = 45))
  538. cowplot::ggsave2("GOBP_30vs60.pdf", width = 6, height = 7)
  541. neuron_60 <- go_60_res[grepl("neuron", go_60_res$Description, ignore.case = TRUE), ]
  542. astro_60 <- go_60_res[grepl("astrocyte|glial", go_60_res$Description, ignore.case = TRUE), ]
  547. head(go_30_res[, c("ID","Description","GeneRatio","p.adjust")])
  548. head(go_60_res[, c("ID","Description","GeneRatio","p.adjust")])
  553. Be_vs_Ctrl_only_neuronastron<-Be_vs_Ctrl_only %>%
  554. filter(gene_symbol %in% c(astro_markers))
  556. RJ_vs_Ctrl_only_neuronastron<-RJ_vs_Ctrl_only %>%
  557. filter(gene_symbol %in% c(astro_markers))
  558. ```

4.Proteomics.Rmd at commit 7179372, under GPL-3.0 · at the source

Overview

Authors: Gabrielle Brum1, Vivian Grizente1, Fábio Luís Lima Monteiro2, Beatriz Luzia de Mello Lima Guimarães1, Livia Goto-Silva1, João Marcos de Azevedo Delou1, Pedro Junior Pinheiro Mourão2, Ismael Carlos da Silva Gomes1,2, Enzo Oliveira Barone2, Matheus Villanueva Andrade2, Rafael Ferreira Lima2, Izabela Mamede3,4, Clarisse Reis3, Fernanda Martins Marim2, Helena Lobo Borges5,6, Michael M Rosenblatt6, Laura Cazañas Torres7,8,9, Fábio César Sousa Nogueira7,8,9, Gilberto Barbosa Domont7,9, Victor Emmanuel Viana Geddes1,4, Renato Santana Aguiar1,2,10,4, Amilcar Tanuri2, Carolina Moreira Voloch2, Stevens Kastrup Rehen1,2,6,4
ORCID iDs: Fábio Luís Lima Monteiro, João Marcos de Azevedo Delou, Enzo Oliveira Barone, Victor Emmanuel Viana Geddes, Stevens Kastrup Rehen
  1. D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
  2. Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
  3. Department of Biochemistry and Immunology, Federal University of Minas Gerais (UFMG), Minas Gerais, Brazil
  4. IDOR Pioneer Science Initiative, Brazil
  5. Institute of Biomedical Sciences, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
  6. R&D Department, Promega Corporation, Madison, WI, United States of America
  7. Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
  8. Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
  9. Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
  10. Department of Genetics, Ecology and Evolution, Federal University of Minas Gerais (UFMG), Minas Gerais, Brazil
Institutions: D’Or Institute for Research and Education (Brazil); Universidade Federal do Rio de Janeiro (Brazil); Universidade Federal de Minas Gerais (Brazil); Promega (United States) (United States); Instituto de Biofísica Carlos Chagas Filho
Journal: EBioMedicine, volume 130, article 106408
Dates: received 22 August 2025; accepted 10 July 2026; published online 30 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.ebiom.2026.106408 · PMID 42531721 · PMCID PMC13450571 · OpenAlex W7171824894
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), developmental (subfield)
Methods: Statistics, Preprocessing
Keywords: Neurodevelopment, Neurotropism, Oropouche virus, Arbovirus infection, Brain organoids
MeSH: Brain*, Bunyaviridae Infections*, Microcephaly*, Organoids*, Orthobunyavirus*, Reassortant Viruses*, Animals, Gene Expression Profiling, Humans, Induced Pluripotent Stem Cells, Neural Stem Cells, Phenotype, Proteomics (* major topic)
Topic: Viral Infections and Outbreaks Research (Infectious Diseases, Medicine), according to OpenAlex
Funding: D&amp;apos;Or Institute for Research and Education; National Council for Scientific and Technological Development (315592/2021-4, INCT-One CNPq 405786/2022-0); Carlos Chagas Filho Foundation for Research Support of Rio de Janeiro State (E-26/201.356/2022, E-26/200.534/2025, E-26/210.773-2021); Promega Corporation; Minas Gerais State Foundation of Support to the Research (APQ-02826-24)
Citations: not cited yet (Europe PMC); 36 references in the paper
Research resources: anti-MAP2 RRID:AB_10982739, anti-Nestin RRID:AB_2155426, anti-PAX6 RRID:AB_2533526, 1:100) and anti-GFAP RRID:AB_880202, anti-cleaved caspase-3 RRID:AB_91556, The human embryonic stem cell line BR-1 RRID:CVCL_C062, RRID:CVCL_F178

Abstract

Background: Oropouche virus (OROV) is an emerging arbovirus currently spreading across South America, with increasing reports of neurological manifestations, severe systemic disease, and congenital abnormalities. Although traditionally associated with mild febrile illness, the recent geographic expansion and surge in OROV outbreaks have prompted attention to its neurotropic potential.

Methods: We investigated the effects of Oropouche virus (OROV) infection on neural stem cells (NSCs) and brain organoids derived from human induced pluripotent stem cells, using both an emergent reassortant isolate and a prototypical strain.

Findings: OROV infected NSCs, leading to cell death, depletion of proliferative progenitors, and disruption of neuroepithelial organisation. Transcriptomic profiling revealed reduction of antiviral response genes and enrichment of signalling pathways related to viral replication, apoptosis, and inhibition of stem cell maintenance and neuronal differentiation. These molecular signatures aligned with phenotypic collapse of progenitor pools and cortical structure observed in organoids, in which OROV infected progenitors, neurons and astrocytes. Proteomic analysis of brain organoids infected with OROV showed regulation of neurodevelopment pathways, which have been previously associated with ZIKV infection, suggesting convergent pathway disruption associated with reduced growth of organoids infected by ZIKV and OROV and highlighting their potential to impair brain development.

Interpretation: These results reveal evidence consistent with a previously unrecognised neurodevelopmental pathogenic potential of OROV strains and provide mechanistic insight into their contribution to microcephaly-like outcomes.

Funding: D'Or Institute, ITpS, CNPq, FAPEMIG, FAPERJ.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

iza-mcac/2025-08-Oropouche-virus-strains-brain

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 717937243aae217e102af508834093ffcc71a0c1, 14 January 2026
Languages: R (4)
Size: 11 files, 4 scripts
Software Heritage: not archived
Found in: the text, “RNA extraction, quantification, library preparat”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (4 files), ggplot2 (4 files), tidyverse (4 files), clusterProfiler (1 file), data.table (1 file), DESeq2 (1 file), Plotly (1 file), Seurat (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/iza-mcac/2025-08-Oropouche-virus-strains-brain

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 6 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 sharing statement

Transcriptomic raw data have been deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1248042 (https://ncbi.nlm.nih.gov/sra?term=PRJNA1248042). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD072533 and PXD076392.

Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026

  • Authors: added João Marcos de Azevedo Delou (0000-0003-1141-5351); Enzo Oliveira Barone (0009-0004-3599-3425); Victor Emmanuel Viana Geddes (0000-0002-0723-3873); removed João Marcos de Azevedo Delou; Enzo Oliveira Barone; Victor Emmanuel Viana Geddes

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 24 authors, 5 keywords, 13 MeSH terms, 5 funders, 35 references, 7 RRIDs.

Cite

This paper

Brum, G., Grizente, V., Lima Monteiro, F. L., Luzia de Mello Lima Guimarães, B., Goto-Silva, L., Marcos de Azevedo Delou, J., Pinheiro Mourão, P. J., Carlos da Silva Gomes, I., Barone, E. O., Andrade, M. V., Lima, R. F., Mamede, I., Reis, C., Marim, F. M., Borges, H. L., Rosenblatt, M. M., Torres, L. C., Sousa Nogueira, F. C., Domont, G. B., . . . Rehen, S. K. (2026). Microcephaly-like phenotype triggered by novel reassortant and prototypic Oropouche virus strains in brain organoids. EBioMedicine, 130, 106408. https://doi.org/10.1016/j.ebiom.2026.106408

BibTeX

@article{brum2026microcephaly,
author = {Brum, Gabrielle and Grizente, Vivian and Lima Monteiro, Fábio Luís and Luzia de Mello Lima Guimarães, Beatriz and Goto-Silva, Livia and Marcos de Azevedo Delou, João and Pinheiro Mourão, Pedro Junior and Carlos da Silva Gomes, Ismael and Barone, Enzo Oliveira and Andrade, Matheus Villanueva and Lima, Rafael Ferreira and Mamede, Izabela and Reis, Clarisse and Marim, Fernanda Martins and Borges, Helena Lobo and Rosenblatt, Michael M and Torres, Laura Cazañas and Sousa Nogueira, Fábio César and Domont, Gilberto Barbosa and Viana Geddes, Victor Emmanuel and Aguiar, Renato Santana and Tanuri, Amilcar and Voloch, Carolina Moreira and Rehen, Stevens Kastrup},
title = {{Microcephaly-like phenotype triggered by novel reassortant and prototypic Oropouche virus strains in brain organoids}},
journal = {EBioMedicine},
year = {2026},
month = jul,
volume = {130},
pages = {106408},
publisher = {Elsevier},
issn = {2352-3964},
doi = {10.1016/j.ebiom.2026.106408},
url = {https://doi.org/10.1016/j.ebiom.2026.106408},
pmid = {42531721},
pmcid = {PMC13450571}
}

RIS

TY - JOUR
AU - Brum, Gabrielle
AU - Grizente, Vivian
AU - Lima Monteiro, Fábio Luís
AU - Luzia de Mello Lima Guimarães, Beatriz
AU - Goto-Silva, Livia
AU - Marcos de Azevedo Delou, João
AU - Pinheiro Mourão, Pedro Junior
AU - Carlos da Silva Gomes, Ismael
AU - Barone, Enzo Oliveira
AU - Andrade, Matheus Villanueva
AU - Lima, Rafael Ferreira
AU - Mamede, Izabela
AU - Reis, Clarisse
AU - Marim, Fernanda Martins
AU - Borges, Helena Lobo
AU - Rosenblatt, Michael M
AU - Torres, Laura Cazañas
AU - Sousa Nogueira, Fábio César
AU - Domont, Gilberto Barbosa
AU - Viana Geddes, Victor Emmanuel
AU - Aguiar, Renato Santana
AU - Tanuri, Amilcar
AU - Voloch, Carolina Moreira
AU - Rehen, Stevens Kastrup
TI - Microcephaly-like phenotype triggered by novel reassortant and prototypic Oropouche virus strains in brain organoids
T2 - EBioMedicine
J2 - EBioMedicine
PY - 2026
DA - 2026/07/30
VL - 130
SP - 106408
SN - 2352-3964
PB - Elsevier
DO - 10.1016/j.ebiom.2026.106408
UR - https://doi.org/10.1016/j.ebiom.2026.106408
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.ebiom.2026.106408",
"type": "article-journal",
"title": "Microcephaly-like phenotype triggered by novel reassortant and prototypic Oropouche virus strains in brain organoids",
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"author": [
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30
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}

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