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Tryptophan-kynurenine metabolic reprogramming along the gut-brain axis alleviates Alzheimer's pathology.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › In vivo gut permeability serum FITC-dextran quantification › Metabolomics ↔ Scripts/Visualization.R, lines 1–53 · score 0.61 · ComplexHeatmap, LMSstat, score, PCA, metabolic
  2. [2] § Materials and methods › In vivo gut permeability serum FITC-dextran quantification › Metabolomics ↔ Scripts/Correlation.R, lines 37–90 · score 0.54 · ComplexHeatmap, circlize, Spearman, coefficients, Correlation

Paper

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The authors' code

R · 161 lines · 5.7 KB · MIT · 1 match

  1. ###############################################################################
  2. # Title: Visualization workflow — group comparisons, volcano plots, and PCA
  3. # Author: Yumi Kim
  4. # Purpose:
  5. # - Run two predefined group comparisons on a metabolomics-like dataset
  6. # - Visualize results as boxplots and volcano plots
  7. # - Perform PCA, then test group differences on PC scores and visualize them
  8. #
  9. # Inputs:
  10. # - CSV file: "Example data/Vis_Example.csv"
  11. # * Column 1: Sample (sample identifier)
  12. # * Column 2: Group (group label; used for comparisons and plotting)
  13. # * Columns 3+: numeric variables/features
  14. #
  15. # Dependencies:
  16. # - Custom functions sourced from "R/Vis_Function.R":
  17. # YMSTAT, Boxplot_ym, Volcano_YM, Volcano_YM_S, PCA_YM_fviz, Boxplot_ym_pc
  18. # - R packages: readxl, dplyr, grid, LMSstat, writexl, devtools, ComplexHeatmap
  19. # (Your Vis_Function.R may use additional packages internally.)
  20. #
  22. ###############################################################################
  24. # 0) Load visualization/stat helper functions
  25. source("R/Vis_Function.R")
  27. # 1) Load required packages (some of these may be used inside Vis_Function.R)
  28. library(readxl) # reading Excel files (not used directly here, but kept)
  29. library(dplyr) # data manipulation (used for piping/verbs)
  30. library(grid) # low-level plotting utilities (used by ggplot/heatmap)
  31. library(LMSstat) # (if used inside Vis_Function.R)
  32. library(writexl) # write xlsx (if Vis_Function.R exports results)
  33. library(devtools) # dev tooling (if Vis_Function.R calls dev helpers)
  34. library(ComplexHeatmap) # complex heatmaps (if used in Vis_Function.R)
  36. # 2) Read data
  37. # - check.names = FALSE keeps original column names (e.g., "AD^APT+F414")
  38. data <- read.csv("Example data/Vis_Example.csv", check.names = FALSE)
  40. # 3) Run statistics for two group comparisons using YMSTAT
  41. # - group_combinations: list of two pairwise contrasts
  42. # * "AD^APT" vs "AD^WT"
  43. # * "AD^APT+F414" vs "AD^APT"
  44. # - Adjust_p_value = FALSE: no multiple-testing correction at this stage
  45. Statfile <- YMSTAT(
  46. data,
  47. group_combinations = list(
  48. c("AD^APT", "AD^WT"),
  49. c("AD^APT+F414", "AD^APT")
  50. ),
  51. Adjust_p_value = FALSE,
  52. Adjust_method = "BH"
  53. )
  55. # 4) Box plot of variables across groups
  56. # - asterisk = "u_test": annotate using Mann–Whitney U-test results from YMSTAT
  57. # - color: manual color palette for the groups (ensure order matches `order=`)
  58. # - significant_variable_only = FALSE: plot all variables, not only significant
  59. # - fig_height/fig_width: final device size (if the function saves plots)
  60. Boxplot_ym(
  61. Statfile,
  62. asterisk = "u_test", # NOTE: If YMSTAT uses "u-test", change this accordingly.
  63. color = c("#323232", "#808080", "#a00000"),
  64. T_size = 30,
  65. X_text = 22,
  66. Y_text = 22,
  67. width = 0.45,
  68. significant_variable_only = FALSE,
  69. label_size = 5.5,
  70. size = 0.7,
  71. fig_height = 9,
  72. fig_width = 6,
  73. order = c("AD^WT", "AD^APT", "AD^APT+F414"), # Ensure these match data$Group exactly
  74. x_text_bold = "bold",
  75. x_text_angle = 45,
  76. y_text_bold = "bold"
  77. )
  79. # 5) Volcano plots
  80. # - `comb` defines which contrasts to visualize
  81. # - `asterisk` MUST match the YMSTAT column name for U-test:
  82. # If YMSTAT outputs "u_test", use "u_test"; if "u-test", use "u-test".
  83. Volcano_YM(
  84. Statfile,
  85. asterisk = "u_test", # <-- check your YMSTAT output column name
  86. comb = list(
  87. c("AD^APT", "AD^WT"),
  88. c("AD^APT+F414", "AD^APT")
  89. )
  90. )
  92. # - Significant-only volcano (e.g., highlights variables passing a threshold)
  93. Volcano_YM_S(
  94. Statfile,
  95. asterisk = "u_test", # <-- same naming note as above
  96. comb = list(
  97. c("AD^APT", "AD^WT"),
  98. c("AD^APT+F414", "AD^APT")
  99. )
  100. )
  102. # 6) PCA
  103. # - PCA_YM_fviz: runs PCA and returns an object containing coordinates, etc.
  104. # - color/order: ensure the group order matches your legend and plotting order
  105. # - legend_position: where to place the legend on the final figure
  106. pca <- PCA_YM_fviz(
  107. data,
  108. color = c("#323232", "#808080", "#a00000"),
  109. order = c("AD^WT", "AD^APT", "AD^APT+F414"),
  110. title = "Title",
  111. legend_position = "bottom"
  112. )
  114. # Extract grouping columns for later merging with PC scores
  115. group <- data[1:2] # assumes col1 = Sample, col2 = Group
  117. # NOTE: Many PCA objects store coordinates as a data.frame or matrix.
  118. # Using `[1]` returns only the first element; to extract the full
  119. # PC1/PC2 columns, you typically want `pca$coordinates[, 1]` and `[, 2]`.
  120. # Keeping your original lines below for consistency:
  121. PC1 <- pca$coordinates[1]
  122. PC2 <- pca$coordinates[2]
  124. # Build a data.frame with Sample, Group, PC1, PC2
  125. PC_DATA <- cbind(group, PC1, PC2)
  127. # 7) Statistics on PC scores and PC score box plots
  128. # - Run YMSTAT on PC1/PC2 (two comparisons as above)
  129. Statfile_pc <- YMSTAT(
  130. PC_DATA,
  131. group_combinations = list(
  132. c("AD^APT", "AD^WT"),
  133. c("AD^APT+F414", "AD^APT")
  134. ),
  135. Adjust_p_value = FALSE
  136. )
  138. # - Boxplots of PC scores
  139. # - asterisk = "t_test": annotate using t-test p-values from YMSTAT
  140. # - order: Make sure group names here match your data exactly.
  141. Boxplot_ym_pc(
  142. Statfile_pc,
  143. asterisk = "t_test",
  144. color = c("#323232", "#808080", "#a00000"),
  145. T_size = 30,
  146. X_text = 22,
  147. Y_text = 22,
  148. width = 0.45,
  149. significant_variable_only = FALSE,
  150. label_size = 5,
  151. size = 0.7,
  152. fig_height = 10,
  153. fig_width = 4,
  154. order = c("AD^WT", "AD^APT", "AD^APT+CKDB001"), # <-- If your data uses "AD^APT+F414", align this label.
  155. x_text_bold = "bold",
  156. x_text_angle = 45,
  157. y_text_bold = "bold"
  158. )
  160. ###############################################################################
  161. ###############################################################################

Visualization.R at commit 2457d89, under MIT · at the source

Overview

Authors: Hyunjung Choi1, Seok Beom Hong1,2, Yumi Kim3, Hyunchae Joung4, Yukyung Choi4, Jiah Cha4, Ji Yong Park5,6,7,8, Yun-Sang Lee5,6,7,8,9, Hayoung Choi1,2, Jong Won Han1,2, Kyung Hwan Kim4, Chang Hun Shin4, Do Yup Lee3,10, Inhee Mook-Jung1,2
ORCID iDs: Hyunjung Choi, Seok Beom Hong, Inhee Mook-Jung
  1. Convergence Dementia Research Center, Medical Research Center, Seoul National University,Seoul, 03080 Republic of Korea
  2. Department of Biomedical Science, College of Medicine, Seoul National University,Seoul, 03080 Republic of Korea
  3. Department of Agricultural Biotechnology, Seoul National University,Seoul, Republic of Korea
  4. Central Research Institute, Chong Kun Dang Bio, Ansan, 15604 Republic of Korea
  5. Department of Nuclear Medicine, Seoul National University Hospital,Seoul, 03080 Republic of Korea
  6. Cancer Research Institute, Seoul National University,Seoul, 03080 Republic of Korea
  7. Department of Nuclear Medicine, College of Medicine, Seoul National University,Seoul, 03080 Republic of Korea
  8. Institute of Radiation Medicine, Medical Research Center, College of Medicine, Seoul National University,Seoul, 03080 Republic of Korea
  9. Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University,08826 Seoul, Republic of Korea
  10. Center for Food and Bioconvergence, Research Institute for Agricultural and Life Sciences, Interdisciplinary Programs in Agricultural Genomics, Seoul National University,Seoul, 08826 Republic of Korea
Institutions: Seoul National University (South Korea); Chong Kun Dang Bio (South Korea) (South Korea); Seoul National University Hospital (South Korea)
Journal: Journal of neuroinflammation, volume 23, issue 1, article 197
Dates: received 18 December 2025; accepted 28 March 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s12974-026-03796-1 · PMID 42026585 · PMCID PMC13248358 · OpenAlex W7155417750
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Alzheimer’s disease, Gut microbiota, Probiotics, Kynurenic acid, Neuroprotection, Neuroinflammation, Gut–brain axis, Multi-omics
MeSH: Alzheimer Disease*, Brain*, Brain-Gut Axis*, Gastrointestinal Microbiome*, Kynurenine*, Tryptophan*, Animals, Hippocampus, Male, Metabolic Reprogramming, Mice, Mice, Inbred C57BL, Probiotics (* major topic)
Topic: Tryptophan and brain disorders (Biological Psychiatry, Neuroscience), according to OpenAlex
Funding: Korea Dementia Research Center (RS-2022-KH128705, RS-2020-KH106747); National Research Foundation of Korea (RS-2023-00273634)
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

The gut–brain axis influences neuroinflammation and metabolic homeostasis in Alzheimer’s disease (AD). Disruption of gut microbiota and barrier function promotes amyloid and tau pathology via immune and metabolic dysregulation. In this study, Limosilactobacillus fermentum SRK414 (SRK414) was orally administered to ADLPAPT mice, resulting in reduced Aβ and tau pathology and improved cognition. Multi-omics analysis revealed that SRK414 altered gut microbial composition and increased hippocampal kynurenic acid (KYNA), a metabolite linked to neuroimmune regulation. Increased hippocampal KYNA was associated with metabolic changes consistent with enhanced neuronal fatty acid oxidation, reduced lipid accumulation, and suppressed microglial activation, suggesting improved hippocampal homeostasis. In vitro studies further showed that KYNA attenuated tau-related and inflammatory phenotypes. These findings support a link between gut microbial modulation and brain resilience, and suggest that KYNA may contribute to the neuroprotective effects associated with SRK414 treatment. This study highlights metabolites modulated by SRK414 administration as potential mediators of microbiota-based therapeutic effects in AD.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12974-026-03796-1.

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 2 matches between paragraphs and lines of code.

SNUFML/AD-STUDY

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2457d89343dad61c02912a3d24f76ec80af63c18, 11 September 2025
Languages: R (7)
Size: 14 files, 7 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), ComplexHeatmap (3 files), circlize (2 files), data.table (2 files), ggplot2 (2 files), survival (2 files), ggpubr (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
9 files
At the source: github.com/SNUFML/AD-STUDY

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;
  • 7 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The LC–MS/MS data generated during the current study are deposited in the MassIVE public repository under the accession number MSV000098470. The processed data is available in the supplementary materials. The supporting code can be found at the following GitHub page: https://github.com/SNUFML/AD-STUDY. 16S rRNA gene amplicon sequencing data of gut microbiota in ADLP mouse model following L. fermentum SRK414 administration are publicly available in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1322104. Transcriptomic data from colon and brain tissues are available in the NCBI SRA under accession number PRJNA1332269. No custom code was used for 16S rRNA gene amplicon sequencing or transcriptomic analyses; only standard, publicly available bioinformatics tools, as described in the Methods section, were employed.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 8 keywords, 13 MeSH terms, 2 funders, 72 references.

Cite

This paper

Choi, H., Hong, S. B., Kim, Y., Joung, H., Choi, Y., Cha, J., Park, J. Y., Lee, Y.-S., Choi, H., Han, J. W., Kim, K. H., Shin, C. H., Lee, D. Y., & Mook-Jung, I. (2026). Tryptophan-kynurenine metabolic reprogramming along the gut-brain axis alleviates Alzheimer's pathology. Journal of neuroinflammation, 23(1), 197. https://doi.org/10.1186/s12974-026-03796-1

BibTeX

@article{choi2026tryptophan,
author = {Choi, Hyunjung and Hong, Seok Beom and Kim, Yumi and Joung, Hyunchae and Choi, Yukyung and Cha, Jiah and Park, Ji Yong and Lee, Yun-Sang and Choi, Hayoung and Han, Jong Won and Kim, Kyung Hwan and Shin, Chang Hun and Lee, Do Yup and Mook-Jung, Inhee},
title = {{Tryptophan-kynurenine metabolic reprogramming along the gut-brain axis alleviates Alzheimer's pathology}},
journal = {Journal of neuroinflammation},
year = {2026},
month = apr,
volume = {23},
number = {1},
pages = {197},
publisher = {BMC},
issn = {1742-2094},
doi = {10.1186/s12974-026-03796-1},
url = {https://doi.org/10.1186/s12974-026-03796-1},
pmid = {42026585},
pmcid = {PMC13248358}
}

RIS

TY - JOUR
AU - Choi, Hyunjung
AU - Hong, Seok Beom
AU - Kim, Yumi
AU - Joung, Hyunchae
AU - Choi, Yukyung
AU - Cha, Jiah
AU - Park, Ji Yong
AU - Lee, Yun-Sang
AU - Choi, Hayoung
AU - Han, Jong Won
AU - Kim, Kyung Hwan
AU - Shin, Chang Hun
AU - Lee, Do Yup
AU - Mook-Jung, Inhee
TI - Tryptophan-kynurenine metabolic reprogramming along the gut-brain axis alleviates Alzheimer's pathology
T2 - Journal of neuroinflammation
J2 - J Neuroinflammation
PY - 2026
DA - 2026/04/24
VL - 23
IS - 1
SP - 197
SN - 1742-2094
PB - BMC
DO - 10.1186/s12974-026-03796-1
UR - https://doi.org/10.1186/s12974-026-03796-1
LA - en
ER -

CSL-JSON

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}

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