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Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants.

Code ↔ Paper

3 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.

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  1. [1] § STAR★Methods › Quantification and statistical analysis › Metabolome dataset ↔ Metabolome_Analysis.Rmd, lines 29–53 · score 0.75 · Fatty acid assay, Oxysterol assay, Lipid assay, p180, Bile, Metabolome
  2. [2] § STAR★Methods › Quantification and statistical analysis › iMAT analysis ↔ iMAT_Model_Generation.m, lines 1–25 · score 0.54 · Human GEM, COBRA, Gurobi, quartile, geTMM, transcriptome
  3. [3] § STAR★Methods › Quantification and statistical analysis › Reporter metabolite analysis ↔ ReporterMetaboliteAnalysis.m, lines 1–94 · score 0.52 · Human GEM, neighbour, reporter metabolites, interaction, nodes, edge

Paper

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The authors' code

R Markdown · 235 lines · 7 KB · CC-BY-4.0 · 1 match

  1. ---
  2. title: "Metabolomics Analysis"
  3. output:
  4. html_document:
  5. df_print: paged
  6. ---
  8. # Libraries
  9. ```{r}
  10. library(tidyr)
  11. library(dplyr)
  12. library(readxl)
  13. library(vroom)
  14. library("Rgraphviz")
  15. library(pca3d)
  16. library(openxlsx)
  18. ```
  20. # Read metabolomics data and metadata
  21. ```{r}
  22. metabolomics<-read.csv("iPSC_APOE__1_Nov_2022.csv")
  23. #499 metabolites
  24. metabolomics_meta<-read_xlsx("iPSC_APOE_codebook__1_Nov_2022.xlsx", sheet = 1)
  26. ```
  29. # Seperate different assays and calculate LODs
  30. ```{r}
  31. metabolomics_ox<- metabolomics_meta %>% group_by(Assay)%>% filter(Assay == "Oxysterol assay")
  32. metabolom_ox<-metabolomics[,(colnames(metabolomics) %in% metabolomics_ox$metabolite)]
  33. ox_LOD<-min(metabolom_ox,na.rm = T)/2
  35. metabolomics_p180<- metabolomics_meta %>% group_by(Assay)%>% filter(Assay == unique(metabolomics_meta$Assay)[2])
  36. metabolom_p180<-metabolomics[,(colnames(metabolomics) %in% metabolomics_p180$metabolite)]
  37. p180_LOD<-min(metabolom_p180,na.rm = T)/2
  39. metabolomics_bile<- metabolomics_meta %>% group_by(Assay)%>% filter(Assay == unique(metabolomics_meta$Assay)[3])
  40. metabolom_bile<-metabolomics[,(colnames(metabolomics) %in% metabolomics_bile$metabolite)]
  41. bile_LOD<-min(metabolom_bile,na.rm = T)/2
  43. metabolomics_fatty<- metabolomics_meta %>% group_by(Assay)%>% filter(Assay == "Fatty acid assay (free)")
  44. metabolom_fatty<-metabolomics[,(colnames(metabolomics) %in% metabolomics_fatty$metabolite)]
  45. fatty_LOD<-min(metabolom_fatty,na.rm = T)/2
  47. metabolomics_lip<- metabolomics_meta %>% group_by(Assay)%>% filter(Assay == "Lipid assay")
  48. metabolom_lip<-metabolomics[,(colnames(metabolomics) %in% metabolomics_lip$metabolite)]
  49. lip_LOD<-min(metabolom_lip,na.rm = T)/2
  51. metabolomics<-data.frame(metabolomics[,1:8], metabolom_ox, metabolom_p180,metabolom_bile,metabolom_fatty,metabolom_lip)
  53. ```
  54. # Seperate APOE3 and APOE4 samples
  55. ```{r}
  56. ast_ApoE3<- t(metabolomics[grep('^A-3-', metabolomics$idno),9:507])
  57. ast_ApoE4<- t(metabolomics[grep('^A-4-', metabolomics$idno),9:507])
  59. mic_ApoE3<- t(metabolomics[grep('^M-3-', metabolomics$idno),9:507])
  60. mic_ApoE4<- t(metabolomics[grep('^M-4-', metabolomics$idno),9:507])
  62. neuron_ApoE3<- t(metabolomics[grep('^N-3-', metabolomics$idno),9:507])
  63. neuron_ApoE4<- t(metabolomics[grep('^N-4-', metabolomics$idno),9:507])
  65. ast_full<-data.frame(ast_ApoE3,ast_ApoE4)
  66. mic_full<-data.frame(mic_ApoE3,mic_ApoE4)
  67. neuron_full<-data.frame(neuron_ApoE3,neuron_ApoE4)
  69. ```
  71. # Fill NA values with LOD value of each assay. The metabolites that are NA value at more than 30% of the samples were removed.
  73. 1-19 ox \
  74. 20-207 p180 \
  75. 208-227 bile \
  76. 228-259 fatty \
  77. 260-499 lip \
  79. ## Neuron
  80. ```{r}
  81. m=1
  82. neuron_delete<-NULL
  83. for (i in 1:nrow(neuron_full)){
  84. na_neuron<-data.frame(is.na(neuron_full[i,]))%>% dplyr::mutate(sum = rowSums(across(where(is.logical))))
  85. if (na_neuron$sum >2.4){ #30% of all samples
  86. neuron_delete[m]<-i
  87. m=m+1}
  88. else{
  89. if (i<20){
  90. neuron_full[i,is.na(neuron_full[i,])]<-ox_LOD
  91. }
  92. else if (i>19 && i<208){
  93. neuron_full[i,is.na(neuron_full[i,])]<-p180_LOD
  94. }
  95. else if (i>207 && i<228){
  96. neuron_full[i,is.na(neuron_full[i,])]<-bile_LOD
  97. }
  98. else if (i>227 && i<260){
  99. neuron_full[i,is.na(neuron_full[i,])]<-fatty_LOD
  100. }
  101. else if (i>259 && i<500){
  102. neuron_full[i,is.na(neuron_full[i,])]<-lip_LOD
  103. }
  105. }
  106. }
  107. neuron_full<-neuron_full[-neuron_delete,]
  108. ```
  110. ## Astrocyte
  111. ```{r}
  112. m=1
  113. ast_delete<-NULL
  114. for (i in 1:nrow(ast_full)){
  115. na_ast<-data.frame(is.na(ast_full[i,]))%>% dplyr::mutate(sum = rowSums(across(where(is.logical))))
  116. if (na_ast$sum >3){
  117. ast_delete[m]<-i
  118. m=m+1}
  119. else{
  120. if (i<20){
  121. ast_full[i,is.na(ast_full[i,])]<-ox_LOD
  122. }
  123. else if (i>19 && i<208){
  124. ast_full[i,is.na(ast_full[i,])]<-p180_LOD
  125. }
  126. else if (i>207 && i<228){
  127. ast_full[i,is.na(ast_full[i,])]<-bile_LOD
  128. }
  129. else if (i>227 && i<260){
  130. ast_full[i,is.na(ast_full[i,])]<-fatty_LOD
  131. }
  132. else if (i>259 && i<500){
  133. ast_full[i,is.na(ast_full[i,])]<-lip_LOD
  134. }
  136. }
  137. }
  138. ast_full<-ast_full[-ast_delete,]
  140. ```
  142. ## Microglia
  143. ```{r}
  144. m=1
  145. mic_delete<-NULL
  146. for (i in 1:nrow(mic_full)){
  147. na_mic<-data.frame(is.na(mic_full[i,]))%>% dplyr::mutate(sum = rowSums(across(where(is.logical))))
  148. if (na_mic$sum >3.9){
  149. mic_delete[m]<-i
  150. m=m+1}
  151. else{
  152. if (i<20){
  153. mic_full[i,is.na(mic_full[i,])]<-ox_LOD
  154. }
  155. else if (i>19 && i<208){
  156. mic_full[i,is.na(mic_full[i,])]<-p180_LOD
  157. }
  158. else if (i>207 && i<228){
  159. mic_full[i,is.na(mic_full[i,])]<-bile_LOD
  160. }
  161. else if (i>227 && i<260){
  162. mic_full[i,is.na(mic_full[i,])]<-fatty_LOD
  163. }
  164. else if (i>259 && i<500){
  165. mic_full[i,is.na(mic_full[i,])]<-lip_LOD
  166. }
  168. }
  169. }
  170. mic_full<-mic_full[-mic_delete,]
  171. ```
  173. # PCA
  174. ```{r}
  176. b<-prcomp(t(log(ast_full,2)),scale. = T)
  177. gr<-rep(c("APOE3","APOE4"),c(6,4))
  178. pca2d(b, components = c(1,2),group=gr, show.labels = T)
  181. b<-prcomp(t(log(mic_full,2)),scale. = T)
  182. gr<-rep(c("APOE3","APOE4"),c(7,6))
  183. pca2d(b, components = c(1,2),group=gr, show.labels = T)
  186. b<-prcomp(t(log(neuron_full,2)),scale. = T)
  187. gr<-rep(c("APOE3","APOE4"),c(4,4))
  188. pca2d(b, components = c(1,2),group=gr, show.labels = T)
  189. ```
  191. # Distribution Check
  192. ```{r}
  193. qqnorm(log(ast_full[2,],2), pch = 1, frame = FALSE)
  194. qqline(ast_full[2,], col = "steelblue")
  195. hist(log(as.numeric(ast_full[2,]),2))
  196. boxplot(log(t(ast_full[1:5,]),2))
  197. ```
  199. # Student's T-test with log2 transformed data
  200. ```{r}
  202. pval_tt_ast<-apply(log(ast_full,2), 1, function(x) t.test(x[1:6],x[7:10])$p.value)
  203. pval_tt_ast_fc<-data.frame(apply(ast_full, 1, function(x) mean(x[7:10])/mean(x[1:6])))
  204. pval_tt_ast_full<-data.frame(metabolite=rownames(ast_full),ttest_pval=pval_tt_ast, Foldchange=pval_tt_ast_fc)
  205. pval_tt_ast_full<-merge(pval_tt_ast_full, metabolomics_meta, by ="metabolite", all.x = T)
  207. pval_tt_mic<-apply(log(mic_full,2), 1, function(x) t.test(x[1:7],x[8:13])$p.value)
  208. pval_tt_mic_fc<-data.frame(apply(mic_full, 1, function(x) mean(x[8:13])/mean(x[1:7])))
  209. pval_tt_mic_full<-data.frame(metabolite=rownames(mic_full),ttest_pval=pval_tt_mic, Foldchange=pval_tt_mic_fc)
  210. pval_tt_mic_full<-merge(pval_tt_mic_full, metabolomics_meta, by ="metabolite", all.x = T)
  212. pval_tt_neuron<-apply(log(neuron_full,2), 1, function(x) t.test(x[1:4],x[5:8])$p.value)
  213. pval_tt_neuron_fc<-data.frame(apply(neuron_full, 1, function(x) mean(x[5:8])/mean(x[1:4])))
  214. pval_tt_neuron_full<-data.frame(metabolite=rownames(neuron_full),ttest_pval=pval_tt_neuron, Foldchange=pval_tt_neuron_fc)
  215. pval_tt_neuron_full<-merge(pval_tt_neuron_full, metabolomics_meta, by ="metabolite", all.x = T)
  217. ```
  219. # Print differentially expressed metabolites
  221. ```{r}
  222. wb <- createWorkbook("Metabolome_DEA")
  224. addWorksheet(wb, "Neuron")
  225. addWorksheet(wb, "Astrocyte")
  226. addWorksheet(wb, "Microglia")
  228. writeData(wb,sheet = "Neuron",pval_tt_neuron_full, rowNames = F)
  229. writeData(wb,sheet = "Astrocyte",pval_tt_ast_full, rowNames = F)
  230. writeData(wb,sheet = "Microglia",pval_tt_mic_full, rowNames = F)
  233. saveWorkbook(wb, "metabolome_ttest_results.xlsx", overwrite = TRUE)
  235. ```

Metabolome_Analysis.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Dilara Uzuner Odongo1, Roxan A Stephenson2, Linling Cheng2, Linda G Yang2, Priyanka S Narayan2, Tunahan Çakır1, Madhav Thambisetty3
ORCID iDs: Dilara Uzuner Odongo, Linda G Yang
  1. Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
  2. Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
  3. Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Institutions: Gebze Technical University (Türkiye); National Institutes of Health (United States); National Institute of Diabetes and Digestive and Kidney Diseases (United States); National Institute on Aging (United States)
Journal: iScience, volume 29, issue 5, article 115638
Dates: received 14 August 2025; accepted 3 April 2026; published online 7 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115638 · PMID 42164526 · PMCID PMC13185917 · OpenAlex W7151477471
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Systems biology, Omics
Topic: Microbial Metabolic Engineering and Bioproduction (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health and Human Services; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; National Institute of Neurological Disorders and Stroke; National Institutes of Health
Citations: not cited yet (Europe PMC); 95 references in the paper
Research resources: RRID:AB_177521, Purified anti-Tubulin β-3 (TUBB3) RRID:AB_2564645, RRID:AB_2565384, Anti-CX3CR1 RRID:AB_306202, Anti-Iba1 RRID:AB_3148646, RRID:AB_477499, RRID:CVCL_4L66

Abstract

Metabolic dysregulation is a key feature of Alzheimer’s disease (AD) pathogenesis, with the APOE ε4 variant (APOE4) representing the strongest genetic risk factor. In this study, we utilized a metabolite-centric approach to investigate how APOE4 reshapes cellular metabolism across brain cell types. Transcriptomic data from isogenic iPSC-derived neurons, astrocytes, and microglia were integrated into a human genome-scale metabolic model to identify genotype-specific alterations. These findings were validated using metabolomics data from the same cell types. In addition to cholesterol and fatty acid dysregulation, we identified alterations in bile acid biosynthesis, folate metabolism, and thyroid hormone metabolism. Similar metabolic signatures were also detected in human postmortem transcriptomic data. Integrating transcriptomic and metabolomic data enhances the understanding of biological mechanisms underlying APOE4-associated metabolic dysregulation in AD.

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

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Zenodo 16037670

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (3), MATLAB (2)
Size: 5 files, 5 scripts
Software Heritage: not checked
Found in: “Data and code availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), edgeR (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.16037670

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

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data and code availability

Deposited Data: Raw sequencing data of microglia samples and processed read counts have been deposited at GEO: GSE305481 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305481)) and are publicly available. Previously published iPSC data are available under accession GEO: GSE102956 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102956).

Software and Custom Code: All original code for the iMAT and Reporter Metabolite analyses has been deposited at Zenodo and is publicly available at Zenodo Data: https://doi.org/10.5281/zenodo.16037670. The code includes custom scripts for multi-omics integration and visualization.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 6 funders, 93 references, 7 RRIDs.

Cite

This paper

Uzuner Odongo, D., Stephenson, R. A., Cheng, L., Yang, L. G., Narayan, P. S., Çakır, T., & Thambisetty, M. (2026). Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants. iScience, 29(5), 115638. https://doi.org/10.1016/j.isci.2026.115638

BibTeX

@article{uzunerodongo2026genome,
author = {Uzuner Odongo, Dilara and Stephenson, Roxan A and Cheng, Linling and Yang, Linda G and Narayan, Priyanka S and Çakır, Tunahan and Thambisetty, Madhav},
title = {{Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {5},
pages = {115638},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115638},
url = {https://doi.org/10.1016/j.isci.2026.115638},
pmid = {42164526},
pmcid = {PMC13185917}
}

RIS

TY - JOUR
AU - Uzuner Odongo, Dilara
AU - Stephenson, Roxan A
AU - Cheng, Linling
AU - Yang, Linda G
AU - Narayan, Priyanka S
AU - Çakır, Tunahan
AU - Thambisetty, Madhav
TI - Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/07
VL - 29
IS - 5
SP - 115638
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115638
UR - https://doi.org/10.1016/j.isci.2026.115638
LA - en
ER -

CSL-JSON

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