OSCR

DRP1 mutations associated with EMPF1 encephalopathy perturb the transcriptional profile and maturation of cortical neurons.

Code ↔ Paper

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The 39 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Trajectory pattern classification ↔ 02_Analysis/revision/supplements/Supp4.sensitivity_analysis.py, lines 43–139 · score 0.98 · nes_strong, improvement_ratio, worsening_ratio, trajdev_nes, sign flip, Natural_worsening
  2. [2] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Trajectory pattern classification ↔ 01_Scripts/Python/pattern_definitions.py, lines 272–406 · score 0.97 · NES_STRONG, IMPROVEMENT_RATIO, WORSENING_RATIO, trajdev_nes, sign flip, Natural_worsening
  3. [3] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Targeted calcium-signaling gene analysis ↔ 02_Analysis/revision/supplements/5a.filtered_volcano_supplement.R, lines 1–71 · score 0.94 · CACNA1C, AveExpr, ATP2A1, log2 CPM, CACNA1S, CASR
  4. [4] § Results › A focused view on curated biological databases ↔ 02_Analysis/3.7.viz_chord_diagrams.py, lines 1–56 · score 0.94 · REACTOME eukaryotic translation, KEGG translation initiation, CC cytosolic ribosome, BP ribosome biogenesis, cytoplasmic structural ribosome, cytoplasmic ribosome biogenesis
  5. [5] § Results › A trajectory framework for developmental pathway dynamics ↔ 01_Scripts/Python/pattern_definitions.py, lines 272–406 · score 0.93 · normalized enrichment score, trajdev_nes, Passive patterns, Natural_worsening, classifiable trajectory, p.adjust
  6. [6] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Targeted calcium-signaling gene analysis ↔ 02_Analysis/2.6.viz_calcium_genes.R, lines 1–61 · score 0.89 · CACNA1C, ATP2A1, CACNA1S, boxplots, CASR, CALR
  7. [7] § Results › A trajectory framework for developmental pathway dynamics ↔ 02_Analysis/3.7.viz_chord_diagrams.py, lines 1–56 · score 0.86 · leading edge membership, Chord diagrams, fold change, cytosolic ribosomal, cytoplasmic ribosomal, outer
  8. [8] § Results › Calcium pathways resist pathway-level interpretation, but gene-level signals are coherent ↔ 02_Analysis/2.2.viz_mito_translation_cascade.R, lines 1–68 · score 0.84 · ER calcium homeostasis, phosphate oxidase, calcium signal, logFC, B6, pyridoxamine
  9. [9] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Per-replicate pathway enrichment (GSVA) ↔ 02_Analysis/1.6.gsva_analysis.R, lines 412–456 · score 0.80 · GSVA driver classification, mutant_driven, arm moves, ctrl_driven, median, dashboard
  10. [10] § Results › A focused view on curated biological databases ↔ 02_Analysis/revision/supplements/Supp10.replicate_level_gsva.py, lines 1–52 · score 0.79 · broader mitochondrial compensatory, Mito_Ribosome_Assembly, mtDNA_Maintenance, mitochondrial ribosome assembly, OXPHOS, GSVA
  11. [11] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Per-replicate pathway enrichment (GSVA) ↔ 02_Analysis/1.6.gsva_analysis.R, lines 51–111 · score 0.79 · maxSize, minSize, logCPM, MitoCarta, kcdf, Gaussian
  12. [12] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Per-replicate pathway enrichment (GSVA) ↔ 02_Analysis/2.4.viz_critical_period_trajectories_gsva.R, lines 1–66 · score 0.78 · maxSize, minSize, GSVA score, enrichment score, kcdf, Variation
  13. [13] § Results › A trajectory framework for developmental pathway dynamics ↔ 01_Scripts/Python/pattern_definitions.py, lines 1–35 · score 0.77 · passive recovery, active trajectory, Natural worsening, Sign reversal, deteriorating, definitions
  14. [14] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Trajectory pattern classification ↔ 02_Analysis/3.5.viz_trajectory_flow.py, lines 137–278 · score 0.75 · Sign_reversal, NES_Late, Late_onset, NES_Early, NES_TrajDev, Transient
  15. [15] § Results › A focused view on curated biological databases ↔ 02_Analysis/revision/supplements/Supp9.cross_compartment_ribosome_trajectory.py, lines 331–371 · score 0.75 · broader mitochondrial compensatory, Mitochondrial_ribosome_assembly, arcs, machinery, subunits, trace
  16. [16] § Results › Mitochondrial pathways show partial transcriptional compensation ↔ 02_Analysis/2.2.viz_mito_translation_cascade.R, lines 155–199 · score 0.74 · Mitochondrial central dogma, ATP synthase, calcium gene, logFC, Calcium signaling, TrajDev
  17. [17] § Results › A trajectory framework for developmental pathway dynamics ↔ 02_Analysis/1.7.create_master_gsva_table.R, lines 286–346 · score 0.74 · passive recovery, active trajectory, Natural worsening, Sign reversal, deteriorating, adaptive
  18. [18] § Results › Mitochondrial pathways show partial transcriptional compensation ↔ 01_Scripts/Python/semantic_categories.py, lines 160–303 · score 0.74 · electron transport, ATP synthase, mitochondrial translation, respiratory, MitoCarta, OXPHOS
  19. [19] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Trajectory pattern classification ↔ 02_Analysis/revision/supplements/6a.sensitivity_sig_universe.py, lines 162–202 · score 0.73 · Sign_reversal, NES_Late, Late_onset, NES_Early, NES_TrajDev, Transient
  20. [20] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Gene set enrichment analysis ↔ 02_Analysis/1.1.main_pipeline.R, lines 1–43 · score 0.73 · GO Cellular Components, Ontologies, MitoCarta, pipeline, SynGO, BH
  21. [21] § Results › Mitochondrial pathways show partial transcriptional compensation ↔ 02_Analysis/2.2.viz_mito_translation_cascade.R, lines 106–153 · score 0.70 · ATP synthase subunits, assembly factors, catalytic, MitoCarta, translation, OXPHOS
  22. [22] § Results › Calcium pathways resist pathway-level interpretation, but gene-level signals are coherent ↔ 02_Analysis/2.2.viz_mito_translation_cascade.R, lines 1–68 · score 0.66 · calcium channel gamma, ORAI1, STIM1, STIM2, CALB1, CACNG3
  23. [23] § Results › A focused view on curated biological databases ↔ 02_Analysis/revision/supplements/Supp9.cross_compartment_ribosome_trajectory.py, lines 117–166 · score 0.66 · RPS proteins, postsynaptic ribosome, cytoplasmic ribosomal, SynGO, RPL, subunits
  24. [24] § Results › Transcriptomic profiling of DRP1 mutant cortical neurons ↔ 02_Analysis/revision/supplements/5b.maturation_euler.R, lines 1–39 · score 0.66 · Euler diagram, UpSet, maturation contrasts, g32a, r403c, DRP1
  25. [25] § Results › A focused view on curated biological databases ↔ 02_Analysis/3.3.ribosome_upset_plot.py, lines 103–134 · score 0.66 · cytoplasmic ribosomal gene, biogenesis factors, ribosome terms, RPL, RPS, subunits
  26. [26] § Methods › Statistical modelling ↔ 02_Analysis/1.1.main_pipeline.R, lines 134–187 · score 0.63 · sample.weights, voomLmFit, matrix, modelling, genotype
  27. [27] § Results › Transcriptomic profiling of DRP1 mutant cortical neurons ↔ 02_Analysis/revision/supplements/5b.highlighted_upset.R, lines 66–125 · score 0.62 · UpSet, maturation DEGs, maturation contrasts, intersects, G32A, R403C
  28. [28] § Methods › Immunofluorescence ↔ Calcium_Local Normalization_GitHub.ipynb, lines 1–114 · score 0.59 · intensity threshold, Single frame, imported, video, baseline, ROIs
  29. [29] § Results › A focused view on curated biological databases ↔ 02_Analysis/3.10.viz_syngo_running_sum_normalised.R, lines 38–96 · score 0.58 · postsynaptic density, synaptic compartments, SynGO, membranes, cytosol, synapse
  30. [30] § Methods › Downstream analysis ↔ 01_Scripts/R_scripts/read_count_matrix.R, the whole file · a weak match · score 0.57 · filterByExpr, expressed genes, TMM, Library, genotype, filtered
  31. [31] § Mutation-specific trajectory deviations (TrajDev, interaction term) › Gene set enrichment analysis ↔ 02_Analysis/3.7.viz_chord_diagrams.py, lines 250–317 · score 0.57 · WikiPathways, msigdbr, SynGO, CC, BP, Reactome
  32. [32] § Mutation-specific trajectory deviations (TrajDev, interaction term) ↔ 02_Analysis/revision/supplements/generate_DE_counts_FDR_0.1.R, the whole file · a weak match · score 0.56 · generate de, limma, expressed genes, BH, FDR, maturation
  33. [33] § Results › Disrupted synaptic development in DRP1 mutant cortical neurons ↔ 02_Analysis/.deprecated/viz_critical_period_trajectories.R, lines 511–583 · score 0.54 · 35–65, G32A mutant, R403C mutant, 15 %, seq, DIV
  34. [34] § Results › Calcium pathways resist pathway-level interpretation, but gene-level signals are coherent ↔ 02_Analysis/revision/supplements/5a.filtered_volcano_supplement.R, lines 1–71 · score 0.53 · AveExpr, ORAI1, STIM1, STIM2, CALB1, CACNG3
  35. [35] § Results › A focused view on curated biological databases ↔ 02_Analysis/1.6.gsva_analysis.R, lines 412–456 · score 0.53 · GSVA driver classification, ctrl_driven, rescuing, mutations
  36. [36] § Results › A focused view on curated biological databases ↔ 01_Scripts/Python/bump_dashboard/presentation/html_fragments.py, lines 1798–1854 · score 0.53 · GSVA driver verdict, enrichment scores, dashboard, genotype, trajectory
  37. [37] § Results › Abnormal mitochondrial trafficking in DRP1 mutant cortical neurons ↔ src/main/java/za/ac/sun/ee/MEL_Modules.java, lines 424–483 · score 0.52 · fusion events, fission events, MEL
  38. [38] § Results › Transcriptomic profiling of DRP1 mutant cortical neurons ↔ 01_Scripts/Python/config.py, lines 58–98 · score 0.51 · derived cortical neurons, iPSC, modulate, neuronal, trajectory
  39. [39] § Mutation-specific trajectory deviations (TrajDev, interaction term) ↔ 02_Analysis/revision/supplements/5b.maturation_euler.R, lines 1–39 · score 0.51 · threshold robustness, maturation contrast, de, FDR

Paper

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The authors' code

R · 467 lines · 18 KB · MIT · 4 matches

  1. ###############################################################################
  2. ## Mitochondrial & Calcium Signaling Cascade Heatmap ##
  3. ## Focused visualization: Complex V, Calcium Genes, Mt Central Dogma ##
  4. ## ##
  5. ## Style: Matches Panel_C_Expression_Heatmap.pdf (synaptic ribosomes) ##
  6. ## - Hierarchical clustering within modules ##
  7. ## - Narrower logFC scale (-0.6 to 0.6) to reveal subtle patterns ##
  8. ## - Dendrogram showing gene co-regulation ##
  9. ###############################################################################
  11. library(here)
  12. library(dplyr)
  13. library(tidyr)
  14. library(ComplexHeatmap)
  15. library(circlize)
  16. library(grid)
  18. # Load unified color configuration
  19. source(here("01_Scripts/R_scripts/color_config.R"))
  21. message("📂 Loading checkpoints...")
  22. checkpoint_dir <- here("03_Results/02_Analysis/checkpoints")
  23. mitocarta_gsea_results <- readRDS(file.path(checkpoint_dir, "mitocarta_gsea_results.rds"))
  24. fit <- readRDS(file.path(checkpoint_dir, "fit_object.rds"))
  26. # Output directory
  27. out_dir <- here("03_Results/02_Analysis/Plots/Mito_translation_cascade")
  28. dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
  30. message("✓ Checkpoints loaded\n")
  32. ###############################################################################
  33. ## Configuration ##
  34. ###############################################################################
  36. # Trajectory framework contrasts (matching cross-database validation)
  37. all_contrasts <- c(
  38. "G32A_vs_Ctrl_D35", "Maturation_G32A_specific", "G32A_vs_Ctrl_D65",
  39. "R403C_vs_Ctrl_D35", "Maturation_R403C_specific", "R403C_vs_Ctrl_D65"
  40. )
  42. contrast_labels <- c(
  43. "Early", "TrajDev", "Late",
  44. "Early", "TrajDev", "Late"
  45. )
  47. # Calcium genes from main pipeline config - MUST include NNAT and PNPO
  48. # These are genes of specific interest for calcium signaling in the study
  49. CALCIUM_GENES_CONFIG <- c(
  50. "NNAT", # Neuronatin - key imprinted gene, calcium regulation
  52. "CACNG3", # Voltage-dependent calcium channel gamma-3
  53. "CACNA1S", # Voltage-dependent L-type calcium channel alpha-1S
  54. "ATP2A1", # Sarcoplasmic/endoplasmic reticulum calcium ATPase 1
  55. "RYR1", # Ryanodine receptor 1 (skeletal muscle type)
  56. "MYLK3", # Myosin light chain kinase 3
  57. "VDR", # Vitamin D receptor (regulates calcium homeostasis)
  58. "STIM1", # Stromal interaction molecule 1 (ER calcium sensor)
  59. "STIM2", # Stromal interaction molecule 2
  60. "ORAI1", # Calcium release-activated calcium modulator 1
  61. "CALB1", # Calbindin 1 (calcium-binding protein)
  62. "CALR", # Calreticulin (calcium-binding ER protein)
  63. "PNPO" # Pyridoxamine 5'-phosphate oxidase (B6 metabolism, affects calcium)
  64. )
  66. ###############################################################################
  67. ## Helper Functions ##
  68. ###############################################################################
  70. #' Extract core enrichment genes from a GSEA result
  71. #' @param gsea_result A gseaResult object
  72. #' @param pathway_name Pattern or exact name to match
  73. #' @param exact_match If TRUE, use exact string match
  74. #' @return Character vector of gene symbols
  75. extract_pathway_genes <- function(gsea_result, pathway_name, exact_match = FALSE) {
  76. if (is.null(gsea_result)) {
  77. message(sprintf(" WARNING: GSEA result is NULL for '%s'", pathway_name))
  78. return(character(0))
  79. }
  81. df <- gsea_result@result
  83. if (exact_match) {
  84. idx <- df$Description == pathway_name
  85. } else {
  86. idx <- grepl(pathway_name, df$Description, ignore.case = TRUE)
  87. }
  89. if (!any(idx)) {
  90. message(sprintf(" WARNING: No pathway matching '%s'", pathway_name))
  91. return(character(0))
  92. }
  94. # Get the most significant matching pathway
  95. matched <- df[idx, ]
  96. best <- matched[which.min(matched$p.adjust), ]
  98. message(sprintf(" Found: '%s' (NES=%.2f, p.adj=%.2e, n=%d)",
  99. best$Description, best$NES, best$p.adjust, best$setSize))
  101. # Extract core enrichment genes (forward-slash delimited)
  102. genes <- unlist(strsplit(best$core_enrichment, "/"))
  103. return(genes)
  104. }
  106. ###############################################################################
  107. ## Extract logFC Matrix for All Contrasts ##
  108. ###############################################################################
  110. message("📊 Extracting expression data for trajectory framework...\n")
  112. logfc_matrix <- sapply(all_contrasts, function(contrast) {
  113. coef_idx <- which(colnames(fit$coefficients) == contrast)
  114. if (length(coef_idx) == 0) {
  115. warning(sprintf("Contrast '%s' not found in fit object", contrast))
  116. return(rep(NA, nrow(fit$coefficients)))
  117. }
  118. logfc <- fit$coefficients[, coef_idx]
  119. names(logfc) <- rownames(fit$coefficients)
  120. return(logfc)
  121. })
  123. colnames(logfc_matrix) <- contrast_labels
  124. message(sprintf(" Extracted logFC for %d contrasts\n", ncol(logfc_matrix)))
  126. ###############################################################################
  127. ## Extract Gene Sets: 3 FOCUSED MODULES ##
  128. ## 1. ATP Synthase (Complex V) - ATP5* genes from KEGG OXPHOS pathway ##
  129. ## 2. Calcium Signaling - Config genes (NNAT, PNPO, etc.) ##
  130. ## 3. Mitochondrial Central Dogma - MitoCarta pathway ##
  131. ###############################################################################
  133. message("📊 Extracting gene sets for 3 focused modules...\n")
  135. ## MODULE 1: ATP Synthase (Complex V) - TRUE ATP5* genes
  136. ## The GOCC_ATPASE_COMPLEX contains chromatin remodeling ATPases (wrong!)
  137. ## We use curated ATP synthase subunit genes from KEGG OXPHOS pathway
  138. message(" Module 1: ATP Synthase (Complex V) - Curated ATP5* subunits...")
  140. # ATP synthase F1 subunits (catalytic core)
  141. atp_f1 <- c("ATP5F1A", "ATP5F1B", "ATP5F1C", "ATP5F1D", "ATP5F1E")
  142. # ATP synthase FO subunits (proton channel)
  143. atp_fo <- c("ATP5PB", "ATP5MC1", "ATP5MC2", "ATP5MC3", "ATP5PD", "ATP5PF",
  144. "ATP5MF", "ATP5MG", "ATP5PO", "ATP5ME", "ATP5MD")
  145. # ATP synthase peripheral stalk
  146. atp_stalk <- c("ATP5MJ", "ATP5MK", "ATP5ML", "ATP5MF")
  147. # Coupling factors and assembly factors
  148. atp_assembly <- c("ATPAF1", "ATPAF2", "TMEM70", "ATP5IF1")
  150. atp_synthase_genes_curated <- unique(c(atp_f1, atp_fo, atp_stalk, atp_assembly))
  151. atp_synthase_genes <- atp_synthase_genes_curated[atp_synthase_genes_curated %in% rownames(logfc_matrix)]
  152. message(sprintf(" ATP Synthase: %d/%d curated genes found in expression data",
  153. length(atp_synthase_genes), length(atp_synthase_genes_curated)))
  155. ## MODULE 2: Calcium Signaling - Config genes (prioritized, curated list)
  156. message(" Module 2: Calcium Signaling - Config genes (incl. NNAT, PNPO)...")
  157. calcium_genes <- CALCIUM_GENES_CONFIG[CALCIUM_GENES_CONFIG %in% rownames(logfc_matrix)]
  158. message(sprintf(" Calcium genes: %d/%d config genes found in expression data",
  159. length(calcium_genes), length(CALCIUM_GENES_CONFIG)))
  161. # Report which config genes are missing
  162. missing_calcium <- setdiff(CALCIUM_GENES_CONFIG, rownames(logfc_matrix))
  163. if (length(missing_calcium) > 0) {
  164. message(sprintf(" Missing: %s", paste(missing_calcium, collapse = ", ")))
  165. }
  167. ## MODULE 3: Mitochondrial Central Dogma (MitoCarta)
  168. ## Extract core enrichment genes, then limit to top 25 by mean |logFC| across TrajDev
  169. message(" Module 3: Mitochondrial Central Dogma (MitoCarta)...")
  170. central_dogma_genes_all <- extract_pathway_genes(
  171. mitocarta_gsea_results[["Maturation_G32A_specific"]],
  172. "Mitochondrial_central_dogma", exact_match = TRUE
  173. )
  174. central_dogma_genes_present <- central_dogma_genes_all[central_dogma_genes_all %in% rownames(logfc_matrix)]
  176. # Limit to top 25 by mean |logFC| across TrajDev contrasts (like Panel_C uses focused set)
  177. MAX_CENTRAL_DOGMA_GENES <- 25
  178. if (length(central_dogma_genes_present) > MAX_CENTRAL_DOGMA_GENES) {
  179. trajdev_cols <- grep("TrajDev", colnames(logfc_matrix))
  180. mean_abs_fc <- rowMeans(abs(logfc_matrix[central_dogma_genes_present, trajdev_cols, drop = FALSE]), na.rm = TRUE)
  181. central_dogma_genes <- names(sort(mean_abs_fc, decreasing = TRUE)[1:MAX_CENTRAL_DOGMA_GENES])
  182. message(sprintf(" Limiting to top %d genes by |logFC| (from %d total)",
  183. MAX_CENTRAL_DOGMA_GENES, length(central_dogma_genes_present)))
  184. } else {
  185. central_dogma_genes <- central_dogma_genes_present
  186. }
  188. ###############################################################################
  189. ## Combine Gene Sets into 3 Modules ##
  190. ###############################################################################
  192. message("\n📊 Building heatmap data matrix...\n")
  194. # Define modules with clear, descriptive names
  195. all_module_genes <- list(
  196. "ATP Synthase (Complex V)" = atp_synthase_genes,
  197. "Calcium Signaling" = calcium_genes,
  198. "Mt Central Dogma" = central_dogma_genes
  199. )
  201. # Report gene counts
  202. for (mod in names(all_module_genes)) {
  203. message(sprintf(" %s: %d genes", mod, length(all_module_genes[[mod]])))
  204. }
  206. # Build cascade data matrix with module annotation
  207. cascade_data <- data.frame()
  208. module_annotation <- c()
  210. for (module_name in names(all_module_genes)) {
  211. genes <- all_module_genes[[module_name]]
  212. genes_present <- genes[genes %in% rownames(logfc_matrix)]
  214. if (length(genes_present) > 0) {
  215. module_data <- logfc_matrix[genes_present, , drop = FALSE]
  216. cascade_data <- rbind(cascade_data, module_data)
  217. module_annotation <- c(module_annotation, rep(module_name, length(genes_present)))
  218. }
  219. }
  221. message(sprintf("\n Total genes for heatmap: %d\n", nrow(cascade_data)))
  223. ###############################################################################
  224. ## Create Cascade Heatmap (Panel_C style: clustering + dendrogram) ##
  225. ###############################################################################
  227. message("📊 Creating mechanistic cascade heatmap (Panel_C style)...\n")
  229. # Color scheme for logFC - NARROWER SCALE to reveal subtle patterns
  230. # Using -0.6 to 0.6 like Panel_C (synaptic ribosomes)
  231. col_fun <- colorRamp2(c(-0.6, 0, 0.6), c("#2166AC", "#F7F7F7", "#B35806"))
  233. # Module colors - simplified for 3 modules
  234. module_colors <- c(
  235. "ATP Synthase (Complex V)" = "#2E8B57", # Sea green
  236. "Calcium Signaling" = "#DC143C", # Crimson
  237. "Mt Central Dogma" = "#9467BD" # Purple
  238. )
  240. # Convert module annotation to factor with explicit order
  241. module_factor <- factor(module_annotation, levels = names(all_module_genes))
  243. # Row annotation (right side, like Panel_C)
  244. ha_module <- rowAnnotation(
  245. Module = module_factor,
  246. col = list(Module = module_colors),
  247. show_annotation_name = TRUE,
  248. annotation_name_side = "top",
  249. annotation_legend_param = list(
  250. Module = list(
  251. title = "Functional Module",
  252. title_gp = gpar(fontface = "bold", fontsize = 10),
  253. labels_gp = gpar(fontsize = 9)
  254. )
  255. )
  256. )
  258. # Column split for mutations
  259. column_split <- factor(rep(c("G32A", "R403C"), each = 3), levels = c("G32A", "R403C"))
  261. # Figure dimensions - taller to accommodate genes with clustering
  262. n_genes <- nrow(cascade_data)
  263. fig_height <- max(10, n_genes * 0.18 + 2) # Adjusted for better gene label visibility
  264. fig_width <- 6
  266. message(sprintf(" Figure dimensions: %.1f x %.1f inches (%d genes)\n",
  267. fig_width, fig_height, n_genes))
  269. # Create heatmap with CLUSTERING WITHIN MODULES (like Panel_C)
  270. pdf(file.path(out_dir, "Mechanistic_Cascade_Heatmap.pdf"),
  271. width = fig_width, height = fig_height)
  273. ht <- Heatmap(
  274. as.matrix(cascade_data),
  275. name = "logFC",
  276. col = col_fun,
  278. # Row settings - ENABLE CLUSTERING within each module (key difference from before!)
  279. row_names_side = "left",
  280. row_names_gp = gpar(fontsize = 8),
  281. cluster_rows = TRUE, # <-- ENABLE clustering within modules
  282. cluster_row_slices = FALSE, # Don't reorder module slices
  283. show_row_dend = TRUE, # <-- SHOW dendrogram
  284. row_dend_width = unit(10, "mm"), # Dendrogram width
  285. row_split = module_factor,
  286. row_title_gp = gpar(fontface = "bold", fontsize = 10),
  287. row_title_rot = 0,
  288. row_gap = unit(2, "mm"),
  290. # Column settings
  291. cluster_columns = FALSE,
  292. show_column_dend = FALSE,
  293. column_names_gp = gpar(fontsize = 9),
  294. column_split = column_split,
  295. column_title_gp = gpar(fontface = "bold", fontsize = 11),
  296. column_gap = unit(2, "mm"),
  298. # Annotations
  299. right_annotation = ha_module,
  301. # Appearance
  302. border = TRUE,
  303. rect_gp = gpar(col = "gray80", lwd = 0.5),
  305. # Legend
  306. heatmap_legend_param = list(
  307. title = "logFC",
  308. at = c(-0.6, -0.3, 0, 0.3, 0.6),
  309. labels = c("-0.6", "-0.3", "0", "0.3", "0.6"),
  310. legend_height = unit(4, "cm"),
  311. title_gp = gpar(fontface = "bold", fontsize = 10)
  312. )
  313. )
  315. draw(ht,
  316. heatmap_legend_side = "right",
  317. column_title = "Mitochondrial & Calcium Gene Expression Trajectories")
  319. dev.off()
  321. message("✓ Cascade heatmap complete\n")
  323. ###############################################################################
  324. ## Create Module Summary Heatmap ##
  325. ###############################################################################
  327. message("📊 Creating module summary heatmap...\n")
  329. # Calculate mean logFC for each module × contrast
  330. module_stats <- data.frame()
  332. for (module_name in names(all_module_genes)) {
  333. genes <- all_module_genes[[module_name]]
  334. genes_present <- genes[genes %in% rownames(logfc_matrix)]
  336. if (length(genes_present) > 0) {
  337. means <- colMeans(logfc_matrix[genes_present, , drop = FALSE], na.rm = TRUE)
  339. module_stats <- rbind(module_stats, data.frame(
  340. Module = module_name,
  341. Contrast = colnames(logfc_matrix),
  342. Mean_logFC = as.numeric(means),
  343. N_genes = length(genes_present)
  344. ))
  345. }
  346. }
  348. # Create wide format for heatmap - use unique column names
  349. # First, add mutation info to make columns unique
  350. module_stats$ColName <- paste0(
  351. rep(c("G32A", "R403C"), each = 3, times = length(unique(module_stats$Module))),
  352. "_",
  353. module_stats$Contrast
  354. )[1:nrow(module_stats)] # Handle potential length mismatch
  356. # Actually let's do this more carefully
  357. # The contrast names are "Early", "TrajDev", "Late" repeated twice
  358. # We need to make them unique before pivoting
  359. module_stats_unique <- module_stats %>%
  360. mutate(
  361. Mutation = rep(rep(c("G32A", "R403C"), each = 3), times = length(unique(Module))),
  362. ColName = paste0(Mutation, "\n", Contrast)
  363. ) %>%
  364. select(Module, ColName, Mean_logFC) %>%
  365. distinct()
  367. module_wide <- module_stats_unique %>%
  368. pivot_wider(names_from = ColName, values_from = Mean_logFC)
  370. module_mat <- as.matrix(module_wide[, -1])
  371. rownames(module_mat) <- module_wide$Module
  373. # Ensure correct column order
  374. expected_cols <- c("G32A\nEarly", "G32A\nTrajDev", "G32A\nLate",
  375. "R403C\nEarly", "R403C\nTrajDev", "R403C\nLate")
  376. module_mat <- module_mat[, expected_cols[expected_cols %in% colnames(module_mat)]]
  378. # Summary heatmap with same color scale
  379. col_fun_summary <- colorRamp2(c(-0.3, 0, 0.3), c("#2166AC", "#F7F7F7", "#B35806"))
  381. pdf(file.path(out_dir, "Module_Summary_Heatmap.pdf"), width = 7, height = 4)
  383. ht_summary <- Heatmap(
  384. module_mat,
  385. name = "Mean\nlogFC",
  386. col = col_fun_summary,
  387. row_names_side = "left",
  388. row_names_gp = gpar(fontsize = 10),
  389. cluster_rows = FALSE,
  390. cluster_columns = FALSE,
  391. column_names_gp = gpar(fontsize = 8),
  392. column_names_rot = 0,
  393. column_split = factor(rep(c("G32A", "R403C"), each = 3),
  394. levels = c("G32A", "R403C")),
  395. column_title = "Module-Level Summary (Mean logFC)",
  396. column_title_gp = gpar(fontface = "bold", fontsize = 11),
  397. cell_fun = function(j, i, x, y, width, height, fill) {
  398. grid.text(sprintf("%.2f", module_mat[i, j]),
  399. x, y, gp = gpar(fontsize = 8))
  400. },
  401. border = TRUE,
  402. heatmap_legend_param = list(
  403. title = "Mean\nlogFC",
  404. at = c(-0.3, -0.15, 0, 0.15, 0.3),
  405. legend_height = unit(2.5, "cm"),
  406. title_gp = gpar(fontface = "bold", fontsize = 9),
  407. labels_gp = gpar(fontsize = 8)
  408. )
  409. )
  411. draw(ht_summary)
  412. dev.off()
  414. message("✓ Module summary heatmap complete\n")
  416. ###############################################################################
  417. ## Summary Statistics ##
  418. ###############################################################################
  420. message("📝 Generating summary...\n")
  422. cat("\n=== Mitochondrial & Calcium Gene Expression Analysis ===\n\n")
  424. cat("THREE FOCUSED MODULES:\n")
  425. cat(" 1. ATP Synthase (Complex V): Curated ATP5* subunit genes from KEGG hsa00190\n")
  426. cat(" - F1 subunits (catalytic): ATP5F1A/B/C/D/E\n")
  427. cat(" - FO subunits (proton channel): ATP5PB, ATP5MC1-3, ATP5PD/PF/MF/MG/PO/ME/MD\n")
  428. cat(" - Assembly factors: ATPAF1/2, TMEM70\n\n")
  429. cat(" 2. Calcium Signaling: Study-prioritized genes\n")
  430. cat(" - Key targets: NNAT (neuronatin), PNPO\n")
  431. cat(" - Channels: CACNG3, CACNA1S, RYR1\n")
  432. cat(" - ER calcium sensors: STIM1, STIM2, CALR\n")
  433. cat(" - Other: ATP2A1, MYLK3, VDR, CALB1\n\n")
  434. cat(" 3. Mt Central Dogma: MitoCarta pathway (GSEA p.adj=2.14e-13)\n")
  435. cat(" - Top 25 genes by |logFC| from core enrichment\n\n")
  437. cat("Gene counts per module:\n")
  438. for (mod in names(all_module_genes)) {
  439. cat(sprintf(" %s: %d genes\n", mod, length(all_module_genes[[mod]])))
  440. }
  442. cat("\nModule Mean logFC Summary:\n\n")
  443. print(module_stats %>%
  444. select(Module, Contrast, Mean_logFC) %>%
  445. pivot_wider(names_from = Contrast, values_from = Mean_logFC) %>%
  446. as.data.frame(),
  447. row.names = FALSE)
  449. # List actual genes included
  450. cat("\n\nGenes included in each module:\n")
  451. for (mod in names(all_module_genes)) {
  452. genes <- all_module_genes[[mod]]
  453. cat(sprintf("\n%s (%d genes):\n", mod, length(genes)))
  454. cat(sprintf(" %s\n", paste(sort(genes), collapse = ", ")))
  455. }
  457. cat("\n")
  458. message("✅ Mitochondrial & Calcium visualization complete!")
  459. message(sprintf("📁 Output directory: %s", out_dir))
  460. message("\n🎯 KEY OUTPUTS:")
  461. message(" 1. Mechanistic_Cascade_Heatmap.pdf - Gene-level heatmap with clustering")
  462. message(" 2. Module_Summary_Heatmap.pdf - Module-level mean summary")
  463. message("\n🎯 STYLE NOTES:")
  464. message(" - Matches Panel_C_Expression_Heatmap.pdf (synaptic ribosomes)")
  465. message(" - Hierarchical clustering within modules shows co-regulated genes")
  466. message(" - Narrower logFC scale (-0.6 to 0.6) reveals subtle patterns")
  467. message(" - Dendrogram indicates gene co-regulation relationships")

2.2.viz_mito_translation_cascade.R at commit fb2628d, under MIT · at the source

Overview

Authors: T B Baum1, J Costanzo1, C Bodnya1, D P Boulton2, M C Caino2, D Mogilenko3, A Zhelonkin4, V Gama1
ORCID iDs: A Zhelonkin
  1. Vanderbilt University, Cell and Developmental Biology and Vanderbilt Center for Stem Cell Biology, Nashville, TN USA
  2. Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO USA
  3. Vanderbilt University Medical Center, Nashville, TN USA
  4. Ben May Department for Cancer Research, University of Chicago, Chicago, IL USA
Institutions: Vanderbilt University (United States); University of Colorado Anschutz (United States); Vanderbilt University Medical Center (United States); University of Chicago (United States)
Journal: Journal of neurodevelopmental disorders, volume 18, issue 1, article 52
Dates: received 9 September 2024; accepted 29 May 2026; published online 26 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s11689-026-09713-0 · PMID 42350961 · PMCID PMC13563865 · OpenAlex W7165962070
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population)
Methods: fMRI & imaging
Keywords: DRP1, Mitochondria, Mitochondrial fission, Neurons
MeSH: Brain Diseases*, Cerebral Cortex*, Dynamins*, Neurons*, Cells, Cultured, Humans, Induced Pluripotent Stem Cells, Mitochondria, Mitochondrial Dynamics, Mutation, Neurodevelopment (* major topic)
Topic: Mitochondrial Function and Pathology (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health (2R35GM128915-06, R35 GM142774, 1F31HD114431-01, 1F31NS129296-01); NIGMS NIH HHS (R35 GM128915, R35 GM142774); NINDS NIH HHS (F31 NS129296); NIH HHS (1F31HD114431-01, R35 GM142774, 1F31NS129296-01, 2R35GM128915-06); NICHD NIH HHS (P50 HD103537, F31 HD114431)
Citations: cited by 1 paper (Europe PMC); 88 references in the paper
Research resources: Mouse anti-mitochondria [113–1] RRID:AB_10562769, Alexa Fluor-568 anti-mouse IgG RRID:AB_11180865, Alexa Fluor-488 anti-mouse IgG RRID:AB_141607, Rabbit Anti-PNPO RRID:AB_2165814, Rabbit Anti-SYP [YE269] RRID:AB_2286949, Alexa Fluor-568 anti-rabbit IgG RRID:AB_2534017, Alexa Fluor-488 anti-rabbit IgG RRID:AB_2535792, Mouse Anti-PSD95 RRID:AB_2877189, Rabbit anti-TUBB3 RRID:AB_444319, Rabbit Anti-NNAT RRID:AB_776717, The GM23338 RRID:CVCL_F182

Abstract

With the advent of exome sequencing, a growing number of children are being identified with de novo loss-of-function mutations in the dynamin 1-like (DNM1L) gene, which encodes the large GTPase essential for mitochondrial fission, dynamin-related protein 1 (DRP1). Mutations in DRP1 result in severe neurodevelopmental phenotypes, such as developmental delay, optic atrophy, and epileptic encephalopathies. Though it is established that mitochondrial fission is an essential precursor to the rapidly changing metabolic needs of the developing cortex, it is not understood how identified mutations in different domains of DRP1 uniquely disrupt cortical development and synaptic maturation. We leveraged the power of human induced pluripotent stem cells (iPSCs) harboring DRP1 mutations in either the GTPase or stalk domains to model early stages of cortical development in vitro. High-resolution time-lapse imaging of transport in neuronal projections revealed mutation-specific changes in mitochondrial motility of severely hyperfused mitochondrial structures. Transcriptional profiling of mutant DRP1 cortical neurons during maturation also implicated mutation-dependent alterations in synaptic development and gene expression of calcium-regulatory genes. Disruptions in calcium dynamics were confirmed using live functional recordings of 65–200 days in vitro (DIV) mutant DRP1 cortical neurons. These findings strongly suggest that altered mitochondrial morphology in DRP1 mutant neurons leads to pathogenic dysregulation of synaptic development and activity.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s11689-026-09713-0.

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 39 matches between paragraphs and lines of code.

CostanzoJa/Neuronal-Analyses

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a0807dd2a9aae63fdb6dd5bebb6a13be3807a808, 18 May 2026
Languages: Jupyter (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Immunofluorescence”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), scikit-image (2 files), SciPy (2 files), tifffile (2 files), OpenCV (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/CostanzoJa/Neuronal-Analyses.git

mogilenko-lab/gama_vivian_drp1_bulkrnaseq

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fb2628d7ba521568ffda102a53da8184467564fa, 1 July 2026
Languages: Python (58), R (49), Shell (2)
Size: 1,645 files, 109 scripts
Software Heritage: not archived
Found in: “Reproducibility, compute environment and availab”
Holds: README, license file, CITATION.cff, environment (requirements.txt, .devcontainer/devcontainer.json, .devcontainer/docker-compose.yml, .devcontainer/scripts/poststart_sanity.sh), tests, documentation
Not found: continuous integration
Tools: tidyverse (32 files), pandas (31 files), NumPy (24 files), ggplot2 (20 files), Matplotlib (18 files), patchwork (11 files), limma (6 files), circlize (5 files), clusterProfiler (4 files), ComplexHeatmap (4 files), edgeR (4 files), Plotly (4 files), seaborn (3 files), reshape2 (2 files), DESeq2 (1 file), pheatmap (1 file), Scanpy (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
111 files
At the source: github.com/Mogilenko-Lab/Gama_Vivian_DRP1_bulkRNAseq/tree/v2.1.0

Zenodo 20213748

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Reproducibility, compute environment and availab”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

rensutheart/MEL-Fiji-Plugin

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 11ba59cc1571dad3024df552510d26670236f6b2, 27 June 2023
Languages: Java (2)
Size: 17 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Mitochondrial event localizer (MEL)”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/rensutheart/MEL-Fiji-Plugin

Tracing map

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  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data is provided within the manuscript or supplementary information files.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 11 MeSH terms, 5 funders, 85 references, 11 RRIDs.

Cite

This paper

Baum, T. B., Costanzo, J., Bodnya, C., Boulton, D. P., Caino, M. C., Mogilenko, D., Zhelonkin, A., & Gama, V. (2026). DRP1 mutations associated with EMPF1 encephalopathy perturb the transcriptional profile and maturation of cortical neurons. Journal of neurodevelopmental disorders, 18(1), 52. https://doi.org/10.1186/s11689-026-09713-0

BibTeX

@article{baum2026drp1,
author = {Baum, T B and Costanzo, J and Bodnya, C and Boulton, D P and Caino, M C and Mogilenko, D and Zhelonkin, A and Gama, V},
title = {{DRP1 mutations associated with EMPF1 encephalopathy perturb the transcriptional profile and maturation of cortical neurons}},
journal = {Journal of neurodevelopmental disorders},
year = {2026},
month = jun,
volume = {18},
number = {1},
pages = {52},
publisher = {BMC},
issn = {1866-1947},
doi = {10.1186/s11689-026-09713-0},
url = {https://doi.org/10.1186/s11689-026-09713-0},
pmid = {42350961},
pmcid = {PMC13563865}
}

RIS

TY - JOUR
AU - Baum, T B
AU - Costanzo, J
AU - Bodnya, C
AU - Boulton, D P
AU - Caino, M C
AU - Mogilenko, D
AU - Zhelonkin, A
AU - Gama, V
TI - DRP1 mutations associated with EMPF1 encephalopathy perturb the transcriptional profile and maturation of cortical neurons
T2 - Journal of neurodevelopmental disorders
J2 - J Neurodev Disord
PY - 2026
DA - 2026/06/26
VL - 18
IS - 1
SP - 52
SN - 1866-1947
PB - BMC
DO - 10.1186/s11689-026-09713-0
UR - https://doi.org/10.1186/s11689-026-09713-0
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s11689-026-09713-0",
"type": "article-journal",
"title": "DRP1 mutations associated with EMPF1 encephalopathy perturb the transcriptional profile and maturation of cortical neurons",
"container-title": "Journal of neurodevelopmental disorders",
"author": [
{
"family": "Baum",
"given": "T B"
},
{
"family": "Costanzo",
"given": "J"
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{
"family": "Bodnya",
"given": "C"
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],
"container-title-short": "J Neurodev Disord",
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"issue": "1",
"page": "52",
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"PMID": "42350961",
"PMCID": "PMC13563865",
"ISSN": "1866-1947",
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"date-parts": [
[
2026,
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26
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]
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}

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