OSCR

Maternal microbiota shapes brain and neuroendocrine development in piglets

Code ↔ Paper

12 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 12 matches
  1. [1] § METHODS AND MATERIALS › Differential expression and functional enrichment analysis ↔ fig4/fig4.Rmd, lines 17 to 98 · score 0.90 · pAdjustMethod, qvalueCutoff, pvalueCutoff, Biological Process, avg_log2FC, clusterProfiler · Report a mismatch
  2. [2] § METHODS AND MATERIALS › Differential expression and functional enrichment analysis ↔ fig2/fig2.Rmd, lines 49 to 143 · score 0.81 · logfc.threshold, pvalueCutoff, enriched terms, min.pct, avg_log2FC, Seurat · Report a mismatch
  3. [3] § METHODS AND MATERIALS › Correlation and cell enrichment analyses ↔ fig1/fig1.Rmd, lines 17 to 54 · score 0.75 · AverageExpression, correlation matrix, gene expression matrix, correlation coefficients, Seurat, Pearson · Report a mismatch
  4. [4] § RESULTS › Maternal microbiota exposure remodels neuronal states and neuron–glia communication during early brain development ↔ fig4/fig4.Rmd, lines 17 to 98 · score 0.68 · immature neurons, glutamatergic neurons, enriched GO, GABAergic, Ontology, AMY · Report a mismatch
  5. [5] § METHODS AND MATERIALS › Pseudotime trajectory analysis ↔ fig4/go_enrichment.R, lines 1 to 31 · score 0.61 · Immature neurons, glutamatergic neurons, GABAergic, neuronal, cells · Report a mismatch
  6. [6] § METHODS AND MATERIALS › Cell clustering, cell‐type annotation, and subcluster analysis ↔ fig2/fig2.Rmd, lines 49 to 143 · score 0.60 · FindAllMarkers, min.pct, avg_log2FC, Seurat, clustering, genes · Report a mismatch
  7. [7] § METHODS AND MATERIALS › Cell clustering, cell‐type annotation, and subcluster analysis ↔ fig2/GVis.R, lines 50 to 135 · score 0.60 · FindAllMarkers, min.pct, avg_log2FC, Seurat, clustering, genes · Report a mismatch
  8. [8] § RESULTS › Maternal microbiota exposure remodels neuronal states and neuron–glia communication during early brain development ↔ fig4/go_enrichment.R, lines 1 to 31 · score 0.59 · immature neurons, glutamatergic neurons, GABAergic, Ontology, neuronal, GO · Report a mismatch
  9. [9] § RESULTS › Maternal microbiota transplantation remodels regional intestinal transcriptional and metabolic landscapes during early development ↔ fig5/fig5.Rmd, lines 17 to 139 · score 0.56 · log2FC, intestinal segment, ns, TPM, duodenum, Bar · Report a mismatch
  10. [10] § METHODS AND MATERIALS › Correlation and cell enrichment analyses ↔ fig1/plot_cell_correlation.R, lines 1 to 41 · score 0.56 · AverageExpression, expression matrix, correlation, Seurat, Pearson, cell · Report a mismatch
  11. [11] § METHODS AND MATERIALS › Weighted gene co‐expression and protein–protein interaction network analyses ↔ fig1/fig1.Rmd, lines 17 to 54 · score 0.52 · correlation coefficients, hierarchical clustering, transformed, matrix, RNA, heatmaps · Report a mismatch
  12. [12] § METHODS AND MATERIALS › Microbiota inoculation, sterility validation, and sample collection ↔ fig5/fig5.Rmd, lines 17 to 139 · score 0.51 · fecal microbiota, microbiota transplantation, duodenum, cecum, colon, jejunum · Report a mismatch

Paper

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The authors' code

R Markdown · 110 lines · 3.9 KB · MIT · 2 matches

  1. ---
  2. title: "Figure 4. GO Biological Process enrichment of DEGs in neuronal cell types."
  3. date: "2026/9/16"
  4. output: html_document
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = TRUE)
  9. # Clean workspace
  10. rm(list=ls())
  11. # Load setting and functions
  12. source("../script/stat_plot_functions.R")
  13. # Set output directory
  14. output_dir <- "./"
  15. ```
  17. GO Biological Process (BP) enrichment of significant DEGs (|avg_log2FC| >= 0.25, p_val < 0.05) from the DEA results of Fig. 3, for the neuronal cell types GABAergic neurons, Glutamatergic neurons and Immature neurons in each tissue. Enrichment was performed with clusterProfiler::enrichGO (pvalueCutoff = 0.05, qvalueCutoff = 0.2, BH adjustment); the top 5 terms per cell type and tissue are reported. Results are written to `GO_results/GO_BP_top5_<Tissue>.csv` and combined in `GO_results/GO_BP_top5_all_tissues.csv`.
  19. ```{r go_enrichment, echo=TRUE}
  20. suppressMessages({
  21. library(clusterProfiler)
  22. library(org.Ss.eg.db)
  23. library(dplyr)
  24. })
  26. WORK_DIR <- output_dir
  27. OUT_DIR <- file.path(WORK_DIR, "GO_results")
  28. dir.create(OUT_DIR, showWarnings = FALSE, recursive = TRUE)
  30. target_celltypes <- c("GABAergic neurons", "Glutamatergic neurons", "Immature neurons")
  31. fc_cutoff <- 0.25 # DEG thresholds (consistent with Fig. 3 volcano plots)
  32. pval_cutoff <- 0.05
  34. GO_ONTOLOGY <- "BP" # Biological Process
  35. GO_PVAL_CUTOFF <- 0.05
  36. GO_QVAL_CUTOFF <- 0.2
  37. GO_TOP_N <- 5 # top N terms per cell type
  39. csv_pattern <- '_all_DEGs\\.csv$'
  41. run_go_enrichment <- function(gene_list, celltype_name, tissue_label) {
  42. if (length(gene_list) < 5) return(NULL)
  44. # SYMBOL -> ENTREZID
  45. entrez <- tryCatch(bitr(gene_list, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Ss.eg.db), error = function(e) NULL)
  46. if (is.null(entrez) || nrow(entrez) < 5) return(NULL)
  48. go_result <- tryCatch(enrichGO(gene = entrez$ENTREZID, OrgDb = org.Ss.eg.db, ont = GO_ONTOLOGY, pAdjustMethod = "BH", pvalueCutoff = GO_PVAL_CUTOFF, qvalueCutoff = GO_QVAL_CUTOFF, readable = TRUE), error = function(e) NULL)
  49. if (is.null(go_result) || nrow(go_result) == 0) return(NULL)
  51. go_df <- go_result@result %>%
  52. arrange(p.adjust) %>%
  53. slice_head(n = GO_TOP_N)
  55. go_df$CellType <- celltype_name
  56. go_df$Tissue <- tissue_label
  57. return(go_df)
  58. }
  60. plot_files <- list.files(path = WORK_DIR, pattern = csv_pattern, full.names = FALSE)
  61. all_go_results <- list()
  63. for (csv_name in plot_files) {
  64. mydata <- read.csv(file.path(WORK_DIR, csv_name), stringsAsFactors = FALSE)
  65. if (nrow(mydata) == 0) next
  66. if (!"celltype" %in% colnames(mydata)) next # skip files without a celltype column
  68. # Extract tissue label from filename (e.g. AMY, HIP)
  69. tissue_label <- gsub("^MMT_D30_vs_GF_D30_|_all_DEGs\\.csv$", "", csv_name)
  71. sig_data <- subset(mydata,
  72. !is.na(celltype) &
  73. celltype %in% target_celltypes &
  74. abs(avg_log2FC) >= fc_cutoff &
  75. p_val < pval_cutoff
  76. )
  77. if (nrow(sig_data) == 0) next
  79. for (ct in target_celltypes) {
  80. ct_genes <- unique(sig_data$gene[sig_data$celltype == ct])
  81. go_df <- run_go_enrichment(ct_genes, ct, tissue_label)
  82. if (!is.null(go_df)) all_go_results[[paste(csv_name, ct, sep = "_")]] <- go_df
  83. }
  84. }
  86. if (length(all_go_results) > 0) {
  87. go_all <- bind_rows(all_go_results)
  89. # Save top N results per tissue
  90. for (tissue in unique(go_all$Tissue)) {
  91. tissue_df <- go_all %>% filter(Tissue == tissue)
  92. write.csv(tissue_df, file.path(OUT_DIR, paste0("GO_BP_top5_", tissue, ".csv")), row.names = FALSE)
  93. }
  95. # Combined summary table
  96. write.csv(go_all, file.path(OUT_DIR, "GO_BP_top5_all_tissues.csv"), row.names = FALSE)
  97. }
  98. ```
  100. ## Summary table
  102. Top 5 GO BP terms per cell type and tissue (sorted by adjusted p-value):
  104. ```{r show_table, echo=TRUE}
  105. go_file <- file.path(OUT_DIR, "GO_BP_top5_all_tissues.csv")
  106. if (file.exists(go_file)) {
  107. go_tab <- read.csv(go_file, stringsAsFactors = FALSE)
  108. print(head(go_tab[, c("Tissue", "CellType", "ID", "Description", "GeneRatio", "BgRatio", "pvalue", "p.adjust", "qvalue")], 20), row.names = FALSE)
  109. }
  110. ```

fig4.Rmd at commit 2cd6fdf, under MIT · at the source

Overview

Authors: Leli Wang1,2, Chao Yan3, Lei Li4, Yuankun Deng2, Hua Shao5, Kuo Long2, Cimin Long1, Yuanyuan Zhu1, Meiqing Chen1, Jialu Huang6, Jinghua Jiang7, Ziyi Lin4, Yunying Yao8, Tao Yang8, Wenbin Guo8, Liangpeng Ge9, Jing Sun9, Yuchun Ding9, Zuohua Liu9, Mingzhu Yin5, Yulong Yin1,2
  1. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
  2. College of Animal Science and Technology, Hunan Agricultural University, Changsha, China
  3. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi‐omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
  4. Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology, Shenzhen, China
  5. Clinical Research Center, Medical Pathology Center, Cancer Early Detection and Treatment Center and Translational Medicine Research Center, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, China
  6. Hunan Institute of Animal and Veterinary Science, Changsha, China
  7. Institute of Medicinal Plant Development, Beijing, China
  8. Huazhi Biotechnology Co. Ltd, Changsha, China
  9. National Center of Technology Innovation for Pigs, Chongqing Academy of Animal Sciences, Key Laboratory of Pig Industry Science, Ministry of Agriculture, Chongqing, China
Journal: iMeta, article e70182
Dates: received 19 August 2026; accepted 29 September 2026; published online 26 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13702092
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), other (organism), developmental (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency
Keywords: brain, germ free, HPA axis, maternal microbiota, piglet
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

The maternal microbiota plays a critical role in early‐life development, yet how maternal microbiota exposure shapes cellular and neuroendocrine programs during mammalian development remains poorly understood. Here, leveraging germ‐free (GF) and maternal microbiota transplantation (MMT) models, we generated an integrated multi‐organ porcine atlas by combining multi‐regional single‐nucleus RNA sequencing, spatial transcriptomics, bulk RNA sequencing of intestinal tissues, and metabolomics. Across five brain regions and the hypothalamic–pituitary–adrenal (HPA) axis, we profiled 673,623 high‐quality nuclei and revealed microbiota‐dependent remodeling of neuronal and glial states across early postnatal developmental stages. MMT reshaped neuronal maturation programs, altered the distribution of immature neuronal states, and elicited region‐specific transcriptional responses in glutamatergic and GABAergic neurons. MMT also reorganized neuron–glia interaction networks, accompanied by trends toward increased plasma glutamic acid and glutamine levels. Across the gut–brain–neuroendocrine axis, MMT was associated with coordinated remodeling of HPA‐axis cellular communication, stress‐response transcriptional programs, and immune signatures, together with reduced circulating cortisol levels. Integrated intestinal analyses revealed segment‐specific transcriptional and metabolic remodeling, including alterations in tryptophan/indole‐related metabolites and immune‐associated pathways. Cross‐species stratified linkage disequilibrium score regression further identified enrichment of human cognitive and neuropsychiatric trait‐associated genetic signals within cell‐type‐specific gene signatures derived from microbiota‐responsive porcine cell populations. Our study establishes a large‐animal cellular atlas linking maternal microbiota‐responsive remodeling across the brain, gut, and neuroendocrine axis, providing a translational framework for understanding how early‐life microbial signals shape mammalian developmental physiology.

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 12 matches between paragraphs and lines of code.

minwenl203-ctrl/Brain-gut-axis-in-germ-free-piglets

License: MIT
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: 2cd6fdf4014c077fcda0772bc64e9934feea8cae, 17 September 2026
Languages: R (15)
Size: 19 files, 15 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file, environment (environment.yml), 6 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (11 files), ggplot2 (8 files), Seurat (8 files), pheatmap (4 files), clusterProfiler (2 files), ComplexHeatmap (2 files), edgeR (2 files)
Availability: 1 check, the latest on 27 October 2026: the link answers
  • 27 October 2026: the link answers
17 files
At the source: github.com/minwenl203-ctrl/Brain-gut-axis-in-germ-free-piglets

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

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 scripts, each with its path and the digest of its content;
  • 12 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

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

Data availability statement

The data supporting the findings of this study have been deposited in the CNGB Sequence Archive (CNSA) of the China National GeneBank DataBase (CNGBdb) under accession number CNP0010048 (https://db.cngb.org/data_resources/project/CNP0010048). The data that support the findings of this study have been deposited into Spatial Transcript Omics DataBase (STOmics DB) with accession number STT0000218 (https://db.cngb.org/stomics/project/STT0000218). The analysis scripts used in this study are publicly available at https://github.com/minwenl203-ctrl/Brain-gut-axis-in-germ-free-piglets. Supplementary materials (figures, tables, graphical abstract, slides, videos, Chinese translated version, and updated materials) may be found in the online DOI or iMeta Science http://www.imeta.science/.

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

Versions

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Version 1, 27 October 2026: the first record

Recorded: type, language, journal, pages, dates, 21 authors, 5 keywords, 65 references.

Cite

This paper

Wang, L., Yan, C., Li, L., Deng, Y., Shao, H., Long, K., Long, C., Zhu, Y., Chen, M., Huang, J., Jiang, J., Lin, Z., Yao, Y., Yang, T., Guo, W., Ge, L., Sun, J., Ding, Y., Liu, Z., . . . Yin, Y. (2026). Maternal microbiota shapes brain and neuroendocrine development in piglets. iMeta, e70182.

BibTeX

@article{wang2026maternal,
author = {Wang, Leli and Yan, Chao and Li, Lei and Deng, Yuankun and Shao, Hua and Long, Kuo and Long, Cimin and Zhu, Yuanyuan and Chen, Meiqing and Huang, Jialu and Jiang, Jinghua and Lin, Ziyi and Yao, Yunying and Yang, Tao and Guo, Wenbin and Ge, Liangpeng and Sun, Jing and Ding, Yuchun and Liu, Zuohua and Yin, Mingzhu and Yin, Yulong},
title = {{Maternal microbiota shapes brain and neuroendocrine development in piglets}},
journal = {iMeta},
year = {2026},
month = oct,
pages = {e70182},
publisher = {Wiley},
issn = {2770-5986},
pmcid = {PMC13702092}
}

RIS

TY - JOUR
AU - Wang, Leli
AU - Yan, Chao
AU - Li, Lei
AU - Deng, Yuankun
AU - Shao, Hua
AU - Long, Kuo
AU - Long, Cimin
AU - Zhu, Yuanyuan
AU - Chen, Meiqing
AU - Huang, Jialu
AU - Jiang, Jinghua
AU - Lin, Ziyi
AU - Yao, Yunying
AU - Yang, Tao
AU - Guo, Wenbin
AU - Ge, Liangpeng
AU - Sun, Jing
AU - Ding, Yuchun
AU - Liu, Zuohua
AU - Yin, Mingzhu
AU - Yin, Yulong
TI - Maternal microbiota shapes brain and neuroendocrine development in piglets
T2 - iMeta
J2 - Imeta
PY - 2026
DA - 2026/10/26
SP - e70182
SN - 2770-5986
PB - Wiley
LA - en
ER -

CSL-JSON

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