OSCR

Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 188 lines · 5.1 KB · MIT

  1. library(MicrobiotaProcess)
  2. library(phyloseq)
  3. library(tidyverse)
  4. library(ggtree)
  5. library(ggtreeExtra)
  6. library(patchwork)
  7. library(ggnewscale)
  8. library(vegan)
  9. library(coin)
  12. pdf("all_plots.pdf", width = 11.69, height = 11.69)
  14. # -------------------------------------------------------------------------
  15. # colors
  16. # -------------------------------------------------------------------------
  18. cols <- c(
  19. "Co.Donor" = "#56b4e9",
  20. "Co" = "#9cd866",
  21. "BIP.Donor"= "#fcda52",
  22. "BIP" = "#f6659e"
  23. )
  25. cols_o <- scales::alpha(cols, .5)
  26. cols_h <- scales::darken(cols, .2)
  28. tax_cols <- colorRampPalette(c(
  29. "#9cd866","#fcda52","#E69F00","#0072B2","#F0E442",
  30. "#b366d8","#D55E00","gray","#56b4e9","#CC79A7","#009E73"
  31. ))(30)
  33. # -------------------------------------------------------------------------
  34. # functions
  35. # -------------------------------------------------------------------------
  37. import_qiime <- function(prefix){
  38. mp_import_qiime2(
  39. otuqza = file.path("Data","QIIME2", paste0("feature_table", prefix, ".qza")),
  40. taxaqza = file.path("Data","QIIME2", paste0("taxonomic_classification", prefix, ".qza")),
  41. mapfilename = file.path("Data","QIIME2", paste0("metadata", prefix, ".tsv")),
  42. treeqza = file.path("Data","QIIME2", paste0("rooted_tree", prefix, ".qza"))
  43. ) %>%
  44. filter(
  45. !Phylum %in% c("p__un_k__Bacteria","p__Chloroflexi","p__un_k__Unknown"),
  46. !Order %in% "o__Chloroplast",
  47. !Family %in% "f__Mitochondria"
  48. )
  49. }
  51. prep_alpha <- function(x){
  52. x %>%
  53. mp_rrarefy(.abundance = Abundance, raresize = 52500, seed = 123) %>%
  54. mp_cal_rarecurve(.abundance = RareAbundance, chunks = 500) %>%
  55. mp_cal_alpha(.abundance = RareAbundance) %>%
  56. mp_cal_pd_metric(.abundance = RareAbundance)
  57. }
  59. calc_beta <- function(x, method){
  60. x %>%
  61. mp_cal_dist(.abundance = RareAbundance, distmethod = method) %>%
  62. mp_cal_pcoa(.abundance = RareAbundance, distmethod = method)
  63. }
  65. ord_plot <- function(x, title=NULL){
  66. mp_plot_ord(
  67. x, .ord = pcoa, .group = Group,
  68. .color = Group, .size = PD, ellipse = TRUE
  69. ) +
  70. scale_color_manual(values = cols) +
  71. scale_fill_manual(values = cols) +
  72. theme_bw() +
  73. ggtitle(title)
  74. }
  76. # -------------------------------------------------------------------------
  77. # import
  78. # -------------------------------------------------------------------------
  80. mpse <- import_qiime("")
  81. mpse_m <- import_qiime("_mouse")
  83. # -------------------------------------------------------------------------
  84. # alpha diversity
  85. # -------------------------------------------------------------------------
  87. mpse <- prep_alpha(mpse)
  88. mpse_m <- prep_alpha(mpse_m)
  90. alpha_tbl <- as.data.frame(colData(mpse)) |>
  91. select(SID, Group, Observe, Shannon, Pielou, PD)
  93. write.table(alpha_tbl, "alpha_diversity.tsv", sep="\t", quote=FALSE, col.names=NA)
  95. p_alpha_human <- mp_plot_rarecurve(
  96. mpse,
  97. .rare = RareAbundanceRarecurve,
  98. .alpha = Observe,
  99. .group = Group,
  100. plot.group = TRUE
  101. ) +
  102. scale_color_manual(values = cols)
  104. p_alpha_mouse <- mp_plot_alpha(
  105. mpse_m,
  106. .alpha = c(Observe, Shannon, PD, Pielou),
  107. .group = Group
  108. ) +
  109. scale_fill_manual(values = cols_o)
  111. (p_alpha_human | p_alpha_mouse)
  113. # -------------------------------------------------------------------------
  114. # beta diversity
  115. # -------------------------------------------------------------------------
  117. methods <- c("bray","unifrac","wunifrac")
  119. beta_stats <- map_dfr(methods, function(m){
  121. tmp <- calc_beta(mpse_m, m)
  123. tmp %<>%
  124. mp_adonis(.abundance = RareAbundance,
  125. distmethod = m,
  126. .formula = ~ Group,
  127. permutation = 999)
  129. stat <- tmp %>%
  130. mp_extract_internal_attr(name = adonis) %>%
  131. mp_fortify()
  133. stat$method <- m
  135. ord_plot(tmp, toupper(m))
  137. stat
  138. })
  140. write.table(beta_stats, "beta_diversity.tsv", sep="\t", quote=FALSE, col.names=NA)
  142. # -------------------------------------------------------------------------
  143. # abundance
  144. # -------------------------------------------------------------------------
  146. mpse %<>%
  147. mp_cal_abundance(.abundance = Abundance, relative = TRUE, force = TRUE) %>%
  148. mp_cal_abundance(.abundance = Abundance, .group = Group, relative = TRUE, force = TRUE)
  150. p_abund <-
  151. mp_plot_abundance(
  152. mpse,
  153. .abundance = Abundance,
  154. taxa.class = Genus,
  155. topn = 30,
  156. .group = Group,
  157. plot.group = TRUE
  158. ) +
  159. scale_fill_manual(values = tax_cols)
  161. p_abund
  163. # -------------------------------------------------------------------------
  164. # differential abundance (mouse)
  165. # -------------------------------------------------------------------------
  167. mpse_m_da <- import_qiime("_mouse") %>%
  168. mp_filter_taxa(.abundance = Abundance, min.abun = 10, min.prop = 0.1) %>%
  169. mp_cal_abundance(.abundance = Abundance, relative = TRUE, force = TRUE)
  171. mpse_m_da %<>%
  172. mp_diff_analysis(
  173. .abundance = RelAbundanceBySample,
  174. .group = Group,
  175. tip.level = "Species",
  176. first.test.method = "kruskal_test",
  177. second.test.method = "wilcox_test",
  178. ml.method = "lda",
  179. ldascore = 3,
  180. action = "add"
  181. )
  183. mp_plot_diff_boxplot(mpse_m_da, .group = Group) +
  184. set_diff_boxplot_color(values = cols)
  186. # -------------------------------------------------------------------------
  188. dev.off()

16s_processing.R at commit 2d689c6, under MIT · at the source

Overview

Authors: Aitak Farzi1, Marija Durdevic1, Frederike Fellendorf1, Patrick Schimmel1, Torben Kuehnast1, Grace Bukowski-Thall1, Sarah Gorkiewicz1, Chi Kin Ip1, Hansjörg Habisch1, Sophia Fischer1, Sabrina Mörkl1, Jolana Wagner-Skacel1, Susanne Bengesser1, Melanie Lenger1, Nina Dalkner1, Christoph Hoegenauer1, Tobias Madl1, Christine Moissl-Eichinger1, Gregor Gorkiewicz1, Eva Reininghaus1
ORCID iDs: Aitak Farzi, Frederike Fellendorf, Sarah Gorkiewicz, Sabrina Mörkl, Nina Dalkner, Eva Reininghaus
  1. Medical University of Graz
Institutions: Medical University of Graz (Austria)
Journal: Research Square (preprint)
Dates: published online 11 May 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9474472/v1 · OpenAlex W7160854308
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Connectivity, Machine learning, fMRI & imaging
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

This study performs patient-to-mouse fecal microbiota transplantation (FMT) as an experimental platform to investigate gut-brain axis alterations with potential relevance to psychiatric disorders, integrating metabolic modeling with measured metabolites and multi-layer molecular profiling. Microbial communities of a stool sample associated with bipolar disorder (BD) displayed a reduced ecological diversity and diminished metabolic potential, particularly within glutamate, aspartate, and GABA biosynthetic pathways. Upon transplantation with BD patient microbiota, recipient mice displayed a markedly altered microbiome characterized by loss of Akkermansia and expansion of Alloprevotella, alongside disruptions in amino acid and carbohydrate metabolism not evident in mice colonized by a healthy donor microbiota. Metabolic microbiome alterations in BD-recipient mice were also correlated to reduced glutathione levels in gut tissue, likely indicating increased oxidative stress, and decreased mRNA expression of key enteroendocrine hormones, including peptide YY and glucagon. Brain metabolomic profiling of BD-recipient mice revealed significant depletion of glycine, choline, and methionine levels connected to anxiety-like phenotypes in elevated plus-maze and light-dark box behavioral tests. Akkermansia abundance positively correlated with physical activity and exploratory behavior, highlighting an important role of this taxon in gut-brain signaling. Collectively, these findings identify distinct microbial, metabolic, and neurobehavioral signatures transmittable from humans to mice via FMT and demonstrate that differences in donor microbiome diversity and metabolic capacity shape engraftment dynamics in recipient mice, which contribute to differences in gut-brain signaling.

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

Repository

Its files are read in the Code ↔ Paper reader above.

marijazmf/BP_paper_analysis

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2d689c64d7f4e71af7d7a83dae2e77b89b961564, 6 March 2026
Languages: R (7), Shell (2), Jupyter (1)
Size: 41 files, 10 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), patchwork (5 files), ggplot2 (2 files), ggpubr (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/marijazmf/BP_paper_analysis

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.

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;
  • 10 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 and code availability

Datasets and scripts used in this work are accessible through the GitHub repository: https://github.com/marijazmf/BP_paper_analysis. Microbiome raw data is accessible via project ID: PRJEB90027.

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

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, journal, dates, 20 authors, 79 references.

Cite

This paper

Farzi, A., Durdevic, M., Fellendorf, F., Schimmel, P., Kuehnast, T., Bukowski-Thall, G., Gorkiewicz, S., Ip, C. K., Habisch, H., Fischer, S., Mörkl, S., Wagner-Skacel, J., Bengesser, S., Lenger, M., Dalkner, N., Hoegenauer, C., Madl, T., Moissl-Eichinger, C., Gorkiewicz, G., & Reininghaus, E. (2026). Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9474472/v1

BibTeX

@article{farzi2026donor,
author = {Farzi, Aitak and Durdevic, Marija and Fellendorf, Frederike and Schimmel, Patrick and Kuehnast, Torben and Bukowski-Thall, Grace and Gorkiewicz, Sarah and Ip, Chi Kin and Habisch, Hansjörg and Fischer, Sophia and Mörkl, Sabrina and Wagner-Skacel, Jolana and Bengesser, Susanne and Lenger, Melanie and Dalkner, Nina and Hoegenauer, Christoph and Madl, Tobias and Moissl-Eichinger, Christine and Gorkiewicz, Gregor and Reininghaus, Eva},
title = {{Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice}},
journal = {Research Square (preprint)},
year = {2026},
month = may,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9474472/v1},
url = {https://doi.org/10.21203/rs.3.rs-9474472/v1}
}

RIS

TY - JOUR
AU - Farzi, Aitak
AU - Durdevic, Marija
AU - Fellendorf, Frederike
AU - Schimmel, Patrick
AU - Kuehnast, Torben
AU - Bukowski-Thall, Grace
AU - Gorkiewicz, Sarah
AU - Ip, Chi Kin
AU - Habisch, Hansjörg
AU - Fischer, Sophia
AU - Mörkl, Sabrina
AU - Wagner-Skacel, Jolana
AU - Bengesser, Susanne
AU - Lenger, Melanie
AU - Dalkner, Nina
AU - Hoegenauer, Christoph
AU - Madl, Tobias
AU - Moissl-Eichinger, Christine
AU - Gorkiewicz, Gregor
AU - Reininghaus, Eva
TI - Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/05/11
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9474472/v1
UR - https://doi.org/10.21203/rs.3.rs-9474472/v1
ER -

CSL-JSON

{
"id": "10.21203/rs.3.rs-9474472/v1",
"type": "article",
"title": "Donor Microbiota Metabolic Capacity Determines Engraftment Dynamics and Modulates Gut–Brain Signaling in Recipient Mice",
"container-title": "Research Square (preprint)",
"author": [
{
"family": "Farzi",
"given": "Aitak"
},
{
"family": "Durdevic",
"given": "Marija"
},
{
"family": "Fellendorf",
"given": "Frederike"
},
{
"family": "Schimmel",
"given": "Patrick"
},
{
"family": "Kuehnast",
"given": "Torben"
},
{
"family": "Bukowski-Thall",
"given": "Grace"
},
{
"family": "Gorkiewicz",
"given": "Sarah"
},
{
"family": "Ip",
"given": "Chi Kin"
},
{
"family": "Habisch",
"given": "Hansjörg"
},
{
"family": "Fischer",
"given": "Sophia"
},
{
"family": "Mörkl",
"given": "Sabrina"
},
{
"family": "Wagner-Skacel",
"given": "Jolana"
},
{
"family": "Bengesser",
"given": "Susanne"
},
{
"family": "Lenger",
"given": "Melanie"
},
{
"family": "Dalkner",
"given": "Nina"
},
{
"family": "Hoegenauer",
"given": "Christoph"
},
{
"family": "Madl",
"given": "Tobias"
},
{
"family": "Moissl-Eichinger",
"given": "Christine"
},
{
"family": "Gorkiewicz",
"given": "Gregor"
},
{
"family": "Reininghaus",
"given": "Eva"
}
],
"container-title-short": "Res Sq",
"DOI": "10.21203/rs.3.rs-9474472/v1",
"ISSN": "2693-5015",
"publisher": "Research Square",
"URL": "https://doi.org/10.21203/rs.3.rs-9474472/v1",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.64898/2026.03.09.710596 [code]
Infant gut microbiomes contribute to metabolic states that impact brain function
Journal: bioRxiv (preprint)
In common: seaborn, tidyverse, pandas, 2 other tools, mouse, 3 references
[2] doi:10.1016/j.xcrm.2026.102655 [code]
An integrative multi-omics approach identifies microbiome alterations linked to pathological and behavioral features in autism spectrum disorder.
Journal: Cell reports. Medicine
In common: ggpubr, patchwork, ggplot2, 5 other tools, 1 reference
[3] doi:10.1038/s41586-026-10679-1 [code]
Cortical development dynamics across autism spectrum disorder mouse models.
Journal: Nature
In common: seaborn, pandas, Matplotlib, 1 other tool, mouse, cellular / molecular, author Sarah Gorkiewicz
[4] doi:10.1186/s12974-026-03796-1 [code]
Tryptophan-kynurenine metabolic reprogramming along the gut-brain axis alleviates Alzheimer's pathology.
Journal: Journal of neuroinflammation
In common: ggpubr, ggplot2, tidyverse, mouse, cellular / molecular, 3 references
[5] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular
[6] doi:10.1186/s13073-026-01704-z [code]
Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex.
Journal: Genome medicine
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular
[7] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular
[8] doi:10.1038/s42003-026-10252-6 [code]
RET signaling as a mediator of estrogen receptor positive breast cancer brain metastasis.
Journal: Communications biology
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular
[9] doi:10.1038/s41467-026-71803-3 [code]
Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.
Journal: Nature communications
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular
[10] doi:10.1016/j.isci.2026.115573 [code]
Female cortical cellular mosaicism underlies shared MeCP2 and PCB impacted gene pathways.
Journal: iScience
In common: ggpubr, patchwork, ggplot2, 5 other tools, mouse, cellular / molecular

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.