OSCR

Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes.

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. [1] § Materials and methods › Weighted gene co-expression network analysis (WGCNA) ↔ scripts_Fasting/Bulk RNA/2. WGCNA.Rmd, lines 169–184 · score 0.87 · blockwiseModules, deepSplit, mergeCutHeight, minModuleSize, bulk RNA, power
  2. [2] § Results › Cell type-specific transcriptomic alterations in the mPFC ↔ scripts_Fasting/Spatial transcriptomics/11. SingleCell_Reference_Data.Rmd, lines 245–280 · score 0.79 · Micro PVM, GABA CGE, GABA MGE, single cell, OPC, Oligo
  3. [3] § Results › Cell type-specific transcriptomic alterations in the mPFC ↔ scripts_Fasting/Spatial transcriptomics/11. SingleCell_Reference_Data.Rmd, lines 283–305 · score 0.63 · GABA_MGE, single cell, L2, L5, CT, PT
  4. [4] § Results › WGCNA identifies gene modules with sex-specific responses to maternal fasting ↔ scripts_Fasting/Bulk RNA/2. WGCNA.Rmd, lines 204–220 · score 0.58 · Eigengene adjacency heatmap, module eigengenes, Dendrogram, network, WGCNA
  5. [5] § Materials and methods › Visium HD ST data analysis ↔ scripts_Fasting/Spatial transcriptomics/14. ST_mPFC_CellType_DEGs_MAST.Rmd, lines 60–87 · score 0.57 · FindMarkers, MAST, mPFC, cell, Spatial transcriptomic, FAST
  6. [6] § Materials and methods › DNA methylation analysis ↔ scripts_Fasting/DNA Methylation/17. Methylation.Rmd, lines 26–52 · score 0.57 · DNA methylation, limma, DMPs, positions, probes, models
  7. [7] § Results › Concordance between bulk RNA-seq and ST ↔ scripts_Fasting/Spatial transcriptomics/13. ST_mPFC_Regional_DEGs.Rmd, lines 59–104 · score 0.56 · generated pseudo bulk, mPFC, DESeq2, regional, spatial transcriptomics, genes
  8. [8] § Results › Cell type-specific transcriptomic alterations in the mPFC ↔ scripts_Fasting/Spatial transcriptomics/14. ST_mPFC_CellType_DEGs_MAST.Rmd, lines 132–164 · score 0.56 · GABA_MGE, L2, L5, CT, PT, upregulated
  9. [9] § Results › Epigenetic remodeling in the PFC ↔ scripts_Fasting/DNA Methylation/17. Methylation.Rmd, lines 185–204 · score 0.53 · open sea, DNA methylation, shores, islands
  10. [10] § Materials and methods › 10X FLEX single-cell data analysis ↔ scripts_Fasting/Spatial transcriptomics/11. SingleCell_Reference_Data.Rmd, lines 215–226 · score 0.52 · single cell, resolution, algorithm, Harmony, UMAP, Clusters

Paper

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The authors' code

R Markdown · 335 lines · 9.7 KB · no license · 3 matches

  1. ```{r message=FALSE}
  2. rm(list = ls())
  3. library(Seurat)
  4. library(cowplot)
  5. library(patchwork)
  6. library(tidyverse)
  7. library(SeuratWrappers)
  8. library(ggplot2)
  9. library(harmony)
  10. library(SoupX)
  11. library(Matrix)
  12. library(DropletUtils)
  13. library(scDblFinder)
  14. set.seed(77)
  15. ```
  17. ```{r}
  18. library(future)
  19. options(future.globals.maxSize = 40 * 1024^3)
  21. ```
  23. ### Functions
  25. ```{r}
  26. ## ---- QC ----
  27. pp <- function(obj){
  28. obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^mt-")
  29. bool_vector <- (obj$percent.mt < 5) & (obj$nCount_RNA > 500) & (obj$nFeature_RNA > 400)
  30. obj <- subset(obj, cells = which(bool_vector))
  31. return(obj)
  32. }
  33. ```
  35. ```{r}
  36. Diet <- function(obj) {
  37. obj <- DietSeurat(obj, assays = "RNA", layers = "counts")
  38. return(obj)
  39. }
  40. ```
  42. ```{r}
  43. ## ---- normalization ----
  44. Norm <- function(obj, vars.to.regress = "percent.mt") {
  45. obj <- NormalizeData(obj, normalization.method = "LogNormalize", scale.factor = 10000)
  46. obj <- FindVariableFeatures(obj, selection.method = "vst", nfeatures = 2000)
  47. obj <- ScaleData(obj, vars.to.regress = vars.to.regress)
  48. obj <- RunPCA(obj, assay = "RNA")
  49. return(obj)
  50. }
  51. ```
  54. ```{r}
  55. ## ---- normalization (harmony)----
  56. NormHarmony <- function(obj, vars.to.regress = "percent.mt") {
  57. obj <- NormalizeData(obj, normalization.method = "LogNormalize", scale.factor = 10000)
  58. obj <- FindVariableFeatures(obj, selection.method = "vst", nfeatures = 2000)
  59. obj <- ScaleData(obj, vars.to.regress = vars.to.regress)
  60. obj <- RunPCA(obj, assay = "RNA")
  61. obj <- RunHarmony(obj, group.by.vars = "group", reduction = "pca", assay.use = "RNA",reduction.save = "harmony")
  62. return(obj)
  63. }
  64. ```
  66. ```{r}
  67. PcaUmap <- function(obj, dims = 1:20, graph.name = NULL) {
  68. obj <- FindNeighbors(obj, reduction = "pca", dims = dims, )
  69. obj <- RunUMAP(obj, reduction = "pca", dims = dims)
  70. return(obj)
  71. }
  72. ```
  74. ```{r}
  75. PcaTsne <- function(obj, dims = 1:20, graph.name = NULL) {
  76. obj <- FindNeighbors(obj, reduction = "pca", dims = dims)
  77. obj <- RunTSNE(obj, reduction = "pca", dims = dims)
  78. return(obj)
  79. }
  80. ```
  82. ```{r}
  83. HarmonyUmap <- function(obj, dims = 1:20, graph.name = NULL) {
  84. obj <- FindNeighbors(obj, reduction = "harmony", dims = dims)
  85. obj <- RunUMAP(obj, reduction = "harmony", dims = dims)
  86. return(obj)
  87. }
  88. ```
  90. ```{r}
  91. HarmonyTsne <- function(obj, dims = 1:20, graph.name = NULL) {
  92. obj <- FindNeighbors(obj, reduction = "harmony", dims = dims)
  93. obj <- RunTSNE(obj, reduction = "harmony", dims = dims)
  94. return(obj)
  95. }
  96. ```
  98. ```{r}
  99. ## ---- run emptyDrops ----
  100. rmEmpty <- function(obj, filtered_obj = NULL) {
  101. set.seed(77)
  102. e.out <- emptyDrops(obj,retain = 12000,lower = 15, alpha = NULL)
  103. is.cell <- e.out$FDR <= 0.001
  104. cat("Identified", sum(is.cell, na.rm = TRUE), "cells.\n")
  105. obj <- obj[, which(is.cell)]
  106. obj <- obj[which(rownames(obj) %in% rownames(filtered_obj)),]
  107. obj <- CreateSeuratObject(obj)
  108. obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^mt-")
  109. return(obj)
  110. }
  111. ```
  114. ```{r}
  115. ## ---- SoupX ----
  116. rmSoup <- function(obj, Raw_obj = NULL, group = NULL) {
  117. DefaultAssay(obj) <- "RNA"
  118. toc <- GetAssayData(obj, assay = "RNA", layer = "counts")
  119. tod <- Raw_obj
  120. tod <- tod[which(rownames(tod) %in% rownames(toc)),]
  121. sc <- SoupChannel(tod, toc, calcSoupProfile = T)
  122. sc <- setClusters(sc, setNames(obj$seurat_clusters, colnames(obj)))
  123. sc <- setContaminationFraction(sc, contFrac = 0.2)
  124. sc <- adjustCounts(sc, roundToInt = T)
  125. obj <- CreateSeuratObject(sc, project = group, min.cells = 20)
  126. obj$group <- obj$orig.ident
  127. obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^mt-")
  128. return(obj)
  129. }
  130. ```
  132. ```{r}
  133. ## ---- scDblFinder ----
  134. FindDbl <- function(obj) {
  135. sce <- obj %>% Diet() %>% as.SingleCellExperiment()
  136. sce <- scDblFinder(sce, clusters = obj$seurat_clusters, dbr.sd = 1)
  137. obj$scDblFinder.class <- sce$scDblFinder.class
  138. return(obj)
  139. }
  140. ```
  144. ```{r}
  145. ## ---- remove doublet and low-quality clusters ----
  146. rmLowquality <- function(obj, LowQualityClusters = NULL, rmDoublets = TRUE) {
  147. obj <- pp(obj)
  148. Idents(obj) <- "seurat_clusters"
  149. obj <- subset(obj, idents = LowQualityClusters, invert = TRUE)
  150. cat("Cluster_remain:",table(obj$seurat_clusters), "\n")
  152. if (rmDoublets) {
  153. Idents(obj) <- "scDblFinder.class"
  154. obj <- subset(obj, idents = "singlet")
  155. cat("Cell_remain:",table(obj$scDblFinder.class), "\n")
  156. }
  158. return(obj)
  159. }
  160. ```
  163. ```{r}
  164. ## ---- load data ----
  165. AL_filtered <- Read10X(data.dir ='C:/R/SingleCell_R/AL')
  166. Fast_filtered <- Read10X(data.dir ='C:/R/SingleCell_R/Fast')
  167. AL_raw <- Read10X(data.dir ='C:/R/SingleCell_R/AL_Raw')
  168. Fast_raw <- Read10X(data.dir ='C:/R/SingleCell_R/Fast_Raw')
  169. ```
  171. ```{r}
  172. ## ---- remove empty droplets ----
  173. AL <- AL_raw %>% rmEmpty(filtered_obj = AL_filtered)
  174. Fast <- Fast_raw %>% rmEmpty(filtered_obj = Fast_filtered)
  175. saveRDS(AL, "AL_afterEmpty.rds")
  176. saveRDS(Fast, "Fast_afterEmpty.rds")
  177. ```
  178. ```{r}
  179. AL <- readRDS("AL_afterEmpty.rds")
  180. Fast <- readRDS("Fast_afterEmpty.rds")
  181. ```
  184. ```{r fig.height=12,fig.width=12, fig.dpi=300}
  185. ## ---- main pipeline ----
  186. ALC <- AL %>% Norm() %>% PcaUmap() %>% FindClusters(algorithm = 4, resolution = 0.5) %>%
  187. rmSoup(Raw_obj = AL_raw, group = "Adlib") %>% Norm() %>% PcaUmap() %>% FindClusters(algorithm = 4, resolution = 0.5) %>% FindDbl()
  189. FastC <- Fast %>% Norm() %>% PcaUmap() %>% FindClusters(algorithm = 4, resolution = 0.5) %>%
  190. rmSoup(Raw_obj = Fast_raw, group = "Fast") %>% Norm() %>% PcaUmap() %>% FindClusters(algorithm = 4, resolution = 0.5) %>% FindDbl()
  191. ```
  194. ```{r height=10,fig.width=20, fig.dpi=300}
  195. ## ---- identify low-quality clusters ----
  196. marker.1 <- FindAllMarkers(ALC, assay = "RNA", only.pos = T, min.pct = 0.25, logfc.threshold = 0.25)
  197. marker.1 <- marker.1 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
  199. marker.2 <- FindAllMarkers(FastC, assay = "RNA", only.pos = T, min.pct = 0.25, logfc.threshold = 0.25)
  200. marker.2 <- marker.2 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
  202. (DoHeatmap(ALC, features = marker.1$gene) + NoLegend() + ggtitle('AL') + theme(plot.title = element_text(size = 15, face = "bold"))) +
  203. (DoHeatmap(FastC, features = marker.2$gene) + NoLegend() + ggtitle('Fast') + theme(plot.title = element_text(size = 15, face = "bold")))
  204. ```
  206. ```{r}
  207. ## ---- remove low-quality cells ----
  208. AL_C <- rmLowquality(ALC, LowQualityClusters = c("1", "3"), rmDoublets = T)
  209. AL_C$group <- "AL"
  210. Fast_C <- rmLowquality(FastC, LowQualityClusters = c("1", "2", "3"), rmDoublets = T)
  211. Fast_C$group <- "Fast"
  212. ```
  215. ## integration
  217. ```{r message=FALSE}
  218. comb <- list(AL = AL_C, Fast = Fast_C)
  219. comb <- lapply(comb, Diet)
  220. comb <- merge(x = comb[[1]], y = comb[[2]])
  221. comb <- NormHarmony(comb)
  222. comb <- JoinLayers(comb)
  223. comb <- HarmonyUmap(comb)
  224. comb <- HarmonyTsne(comb)
  225. comb <- FindClusters(comb, algorithm = 4, resolution = 0.5)
  226. ```
  228. ```{r fig.height=6, fig.width=6, fig.dpi=300}
  229. DimPlot(comb, reduction = "tsne", group.by = "seurat_clusters",label = T) + NoLegend()
  230. DimPlot(comb, reduction = "umap", group.by = "seurat_clusters",label = T) + NoLegend()
  231. ```
  234. ## Annotation
  236. ```{r}
  237. Idents(comb) <- "seurat_clusters"
  238. comb.markers <- FindAllMarkers(comb, assay = "RNA", only.pos = TRUE, min.pct = 0.5,
  239. logfc.threshold = 0.5)
  240. comb.top.markers <- comb.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
  241. ```
  242. ```{r fig.height=6,fig.width=12, fig.dpi=300}
  243. DoHeatmap(comb, features = comb.top.markers$gene) + NoLegend() + ggtitle('Comb') +theme(plot.title = element_text(size = 10, face = "bold"))
  244. ```
  245. ```{r}
  246. marker_sets <- list(
  247. Exc_shared = c("Slc17a7"),
  248. Inh_shared = c("Gad1", "Gad2"),
  250. "L2-3_IT" = c("Calb1", "Rasgrf2", "Cux2", "Nectin3"),
  251. "L5-6_IT" = c("Hrh3", "Cnih3", "Dkkl1", "Gfra2"),
  252. CT = c("Nxph3", "Hs3st4", "Syt6", "Pcp4"),
  253. PT = c("Igfbp4", "Bcl6", "Fezf2", "Bcl11b"),
  255. GABA_MGE = c("Lhx6", "Pvalb", "Sst", "Tac1"),
  256. GABA_CGE = c("Vip", "Reln", "Prox1", "Htr3a"),
  258. Ast = c("Aldh1l1", "Aqp4", "Slc1a3", "Gfap"),
  259. Oligo = c("Mbp", "Mog", "Plp1", "Mag"),
  260. OPC = c("Pdgfra", "Cspg4", "Olig1", "Gpr17"),
  261. "Micro-PVM" = c("Tmem119", "P2ry12", "Mrc1", "C1qa"),
  262. Endo = c("Pecam1", "Cldn5", "Cdh5", "Kdr"),
  263. VLMC = c("Col1a1", "Col1a2", "Lum", "Dcn")
  264. )
  266. for (ct in names(marker_sets)) {
  267. genes <- intersect(marker_sets[[ct]], rownames(comb))
  269. for (g in genes) {
  270. p <- FeaturePlot(
  271. object = comb,
  272. features = g,
  273. reduction = "umap",
  274. order = TRUE
  275. ) + ggtitle(paste0(ct, " - ", g))
  277. print(p)
  278. }
  279. }
  280. ```
  283. ```{r}
  284. Idents(comb) <- "seurat_clusters"
  285. new.cluster.ids <- c(
  286. '1' = "L5-6_IT",
  287. '2' = "L2-3_IT",
  288. '3' = "Ast",
  289. '4' = "L2-3_IT",
  290. '5' = "GABA_MGE",
  291. '6' = "CT",
  292. '7' = "VLMC",
  293. '8' = "PT",
  294. '9' = "Ast",
  295. '10' = "Micro-PVM",
  296. '11' = "Endo",
  297. '12' = "GABA_CGE",
  298. '13' = "Oligo",
  299. '14' = "OPC",
  300. '15' = "Oligo",
  301. '16' = "CT",
  302. '17' = "Micro-PVM")
  303. comb <- RenameIdents(comb, new.cluster.ids)
  304. comb$bulktype <- Idents(comb)
  305. ```
  308. ```{r fig.height=8, fig.width=8, fig.dpi=300}
  309. DimPlot(comb, reduction = "umap",group.by = "bulktype", label = T, label.size = 3, repel = T ) + NoLegend() + theme(title = element_text(size = 20))
  310. DimPlot(comb, reduction = "tsne",group.by = "bulktype", label = T, label.size = 3, repel = T ) + NoLegend() + theme(title = element_text(size = 20))
  311. ```
  313. ```{r}
  314. comb.markers <- FindAllMarkers(comb, assay = "RNA", only.pos = TRUE, min.pct = 0.25,
  315. logfc.threshold = 0.25)
  316. comb.top.markers <- comb.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
  317. ```
  318. ```{r fig.height=6,fig.width=12, fig.dpi=300}
  319. DoHeatmap(comb, features = comb.top.markers$gene) + NoLegend() + ggtitle('Comb') +theme(plot.title = element_text(size = 10, face = "bold"))
  320. ```
  322. ```{r}
  323. saveRDS(comb, "renamed_0703.rds")
  324. # rm(AL_raw,Fast_raw,AL_C, Fast_C, AL, Fast, ALC, FastC)
  325. save.image("renamed_0703.RData")
  326. ```
  327. ```{r}
  328. comb <- readRDS("renamed_0703.rds")
  329. ```

11. SingleCell_Reference_Data.Rmd at commit 111a407, no license · at the source

Overview

Authors: Hongbo Wang1, Miki Bundo2, Yutaka Nakachi2, Akinori Kanai3, Yui Yamamoto1, Hirofumi Miyazaki1, Fumiko Toyoshima4, Yasuyuki Shima5, Mai Sakai6, Zhiqian Yu7,8, Hiroaki Tomita7,8, Yutaka Suzuki3, Kazuya Iwamoto2, Yuji Owada1, Motoko Maekawa1
ORCID iDs: Hongbo Wang, Miki Bundo, Yutaka Nakachi, Fumiko Toyoshima, Zhiqian Yu, Kazuya Iwamoto, Motoko Maekawa
  1. Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Sendai, Japan
  2. Department of Molecular Brain Science, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
  3. Life Science Data Research Center, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
  4. Department of Homeostatic Medicine, Medical Research Laboratory, Institute of Science Tokyo, Tokyo, Japan
  5. Neurodegenerative Disorders Collaborative Laboratory, RIKEN Center for Brain Science, Saitama, Japan
  6. Department of Psychiatric Nursing, Graduate School of Medicine, Tohoku University, Sendai, Japan
  7. Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
  8. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
Institutions: Tohoku University (Japan); Kumamoto University (Japan); The University of Tokyo (Japan); Institute of Science Tokyo (Japan); RIKEN Center for Brain Science (Japan); Tohoku Medical Megabank Organization (Japan)
Journal: Molecular psychiatry, volume 31, issue 9, pages 5431-5444
Dates: received 29 September 2025; accepted 30 April 2026; published online 13 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41380-026-03629-w · PMID 42120553 · PMCID PMC13441970 · OpenAlex W7160944125
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), schizophrenia / psychosis (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Schizophrenia, Neuroscience
MeSH: Fasting*, Schizophrenia*, Animals, Developmental Origins of Health and Disease, Disease Models, Animal, DNA Methylation, Epigenesis, Genetic, Epigenomics, Female, Male, Mice, Mice, Inbred C57BL, Oxidative Stress, Phenotype, Prefrontal Cortex, Pregnancy, Prenatal Exposure Delayed Effects (* major topic)
Topic: Tryptophan and brain disorders (Biological Psychiatry, Neuroscience), according to OpenAlex
Funding: MEXT | Japan Society for the Promotion of Science (JSPS) (23K24783, JP22H04925 (PAGS), JP22K19502, JP23K24252, 25K02586)
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Schizophrenia is a severe neurodevelopmental disorder whose etiology remains incompletely understood. Epidemiological studies of the Dutch Hunger Winter demonstrated that maternal famine during early gestation increased the risk of schizophrenia in offspring, implicating the Developmental Origins of Health and Disease (DOHaD) framework. However, the molecular mechanisms underlying this association remain unclear. Here, we developed a novel DOHaD-based schizophrenia model by subjecting pregnant mice to transient fasting restricted to the peri-implantation period, a critical window of global epigenomic reprogramming. Male offspring of fasted dams exhibited schizophrenia-related phenotypes, including impaired sensorimotor gating, abnormal behavioral patterns, and reduced dendritic spine density in the medial prefrontal cortex. Multi-omics profiling, integrating bulk RNA sequencing, Visium HD spatial transcriptomics, and DNA methylation arrays, revealed convergent alterations in synaptic organization, protein homeostasis, and oxidative stress pathways. These findings highlight how brief maternal fasting reprograms the epigenome and reshapes neural circuitry. Our work establishes the first animal model that directly mirrors early gestational famine exposure linked to schizophrenia risk, providing a unique platform for uncovering epigenetic mechanisms underlying the developmental origins of psychiatric disorders.

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

Repository

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

HonboW77/Maternal-fasting

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 111a4076b32656f94c1cfc619d0cfcf8e16b23d9, 16 May 2026
Languages: R (10), Jupyter (6)
Size: 18 files, 16 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: 16 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (10 files), tidyverse (10 files), patchwork (6 files), Seurat (6 files), clusterProfiler (5 files), NumPy (5 files), pandas (5 files), pheatmap (5 files), Plotly (5 files), Matplotlib (4 files), DESeq2 (3 files), Harmony (2 files), limma (2 files), anndata (1 file), circlize (1 file), ComplexHeatmap (1 file), cowplot (1 file), igraph (1 file), reshape2 (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
16 files
At the source: github.com/HonboW77/Maternal-fasting

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

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Data

No dataset and no data link were found in the paper.

Data availability

The raw data obtained in this study are available on DDBJ database (http://www.ddbj.nig.ac.jp/index-e.html) under the BioProject accession number PRJDB40357 (bulk RNA-seq, single-cell transcriptomes and DNA methylation) and PRJDB20747 (spatial transcriptomics). The corresponding BioSample metadata are available under accession numbers SAMD01818712-SAMD01818723 (bulk RNA-seq, male offspring), SAMD01818700-SAMD01818711 (bulk RNA-seq, female offspring), SAMD01695265 (single-cell transcriptomes), SAMD00912252–SAMD00912255 (spatial transcriptomics) and SAMD01688625–SAMD01688636 (DNA methylation). All scripts used for data processing and analysis are available on GitHub: https://github.com/HonboW77/Maternal-fasting.

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

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Nature

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 17 MeSH terms, 1 funder, 96 references.

Cite

This paper

Wang, H., Bundo, M., Nakachi, Y., Kanai, A., Yamamoto, Y., Miyazaki, H., Toyoshima, F., Shima, Y., Sakai, M., Yu, Z., Tomita, H., Suzuki, Y., Iwamoto, K., Owada, Y., & Maekawa, M. (2026). Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes. Molecular psychiatry, 31(9), 5431-5444. https://doi.org/10.1038/s41380-026-03629-w

BibTeX

@article{wang2026maternal,
author = {Wang, Hongbo and Bundo, Miki and Nakachi, Yutaka and Kanai, Akinori and Yamamoto, Yui and Miyazaki, Hirofumi and Toyoshima, Fumiko and Shima, Yasuyuki and Sakai, Mai and Yu, Zhiqian and Tomita, Hiroaki and Suzuki, Yutaka and Iwamoto, Kazuya and Owada, Yuji and Maekawa, Motoko},
title = {{Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes}},
journal = {Molecular psychiatry},
year = {2026},
month = may,
volume = {31},
number = {9},
pages = {5431--5444},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03629-w},
url = {https://doi.org/10.1038/s41380-026-03629-w},
pmid = {42120553},
pmcid = {PMC13441970}
}

RIS

TY - JOUR
AU - Wang, Hongbo
AU - Bundo, Miki
AU - Nakachi, Yutaka
AU - Kanai, Akinori
AU - Yamamoto, Yui
AU - Miyazaki, Hirofumi
AU - Toyoshima, Fumiko
AU - Shima, Yasuyuki
AU - Sakai, Mai
AU - Yu, Zhiqian
AU - Tomita, Hiroaki
AU - Suzuki, Yutaka
AU - Iwamoto, Kazuya
AU - Owada, Yuji
AU - Maekawa, Motoko
TI - Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/05/13
VL - 31
IS - 9
SP - 5431
EP - 5444
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03629-w
UR - https://doi.org/10.1038/s41380-026-03629-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41380-026-03629-w",
"type": "article-journal",
"title": "Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes",
"container-title": "Molecular psychiatry",
"author": [
{
"family": "Wang",
"given": "Hongbo"
},
{
"family": "Bundo",
"given": "Miki"
},
{
"family": "Nakachi",
"given": "Yutaka"
},
{
"family": "Kanai",
"given": "Akinori"
},
{
"family": "Yamamoto",
"given": "Yui"
},
{
"family": "Miyazaki",
"given": "Hirofumi"
},
{
"family": "Toyoshima",
"given": "Fumiko"
},
{
"family": "Shima",
"given": "Yasuyuki"
},
{
"family": "Sakai",
"given": "Mai"
},
{
"family": "Yu",
"given": "Zhiqian"
},
{
"family": "Tomita",
"given": "Hiroaki"
},
{
"family": "Suzuki",
"given": "Yutaka"
},
{
"family": "Iwamoto",
"given": "Kazuya"
},
{
"family": "Owada",
"given": "Yuji"
},
{
"family": "Maekawa",
"given": "Motoko"
}
],
"container-title-short": "Mol Psychiatry",
"volume": "31",
"issue": "9",
"page": "5431-5444",
"DOI": "10.1038/s41380-026-03629-w",
"PMID": "42120553",
"PMCID": "PMC13441970",
"ISSN": "1359-4184",
"publisher": "Springer Nature",
"URL": "https://doi.org/10.1038/s41380-026-03629-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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