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Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression.

A correction to this paper has been published: the notice, 42643565, from Europe PMC.

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  1. [1] § Materials and methods › Validation of cellular profiles via correlation with reference signatures ↔ Deconvolution/Deconvolution.R, lines 1–45 · score 0.59 · Ensembl IDs, gene symbols, Mapping

Paper

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The authors' code

R · 137 lines · 7.6 KB · no license · 1 match

  1. # ─────────────────────────────────────────────────────────────
  2. # 1. Install and load required packages
  3. # ─────────────────────────────────────────────────────────────
  4. if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
  5. BiocManager::install(c("zellkonverter", "SingleCellExperiment", "scater", "org.Hs.eg.db", "AnnotationDbi"))
  7. install.packages(c("reticulate", "pheatmap", "ggplot2", "reshape2", "dplyr", "openxlsx", "readxl"))
  9. library(org.Hs.eg.db)
  10. library(AnnotationDbi)
  11. library(pheatmap)
  12. library(ggplot2)
  13. library(reshape2)
  14. library(dplyr)
  15. library(openxlsx)
  16. library(readxl)
  17. library(CDSeq)
  19. # ─────────────────────────────────────────────────────────────
  20. # 2. Load input data (ASTROCYTES)
  21. # ─────────────────────────────────────────────────────────────
  22. ref_matrix <- read.csv("hippocampus_reference_matrix.csv", row.names = 1, check.names = FALSE)
  23. pseudo_counts <- read.csv("pseudo_counts_geneSymbol_GSE28146_FIXED.csv", sep = ";", row.names = 1, check.names = FALSE)
  25. # ─────────────────────────────────────────────────────────────
  26. # 3. Map ENSEMBL to gene symbols
  27. # ─────────────────────────────────────────────────────────────
  28. ensembl_ids <- rownames(ref_matrix)
  29. gene_symbols <- mapIds(org.Hs.eg.db,
  30. keys = ensembl_ids,
  31. column = "SYMBOL",
  32. keytype = "ENSEMBL",
  33. multiVals = "first")
  35. ref_matrix$GeneSymbol <- gene_symbols
  36. ref_matrix <- ref_matrix[!is.na(ref_matrix$GeneSymbol) & !duplicated(ref_matrix$GeneSymbol), ]
  37. rownames(ref_matrix) <- ref_matrix$GeneSymbol
  38. ref_matrix$GeneSymbol <- NULL
  40. # ─────────────────────────────────────────────────────────────
  41. # 4. Match common genes
  42. # ─────────────────────────────────────────────────────────────
  43. common_genes <- intersect(rownames(ref_matrix), rownames(pseudo_counts))
  44. ref_common <- ref_matrix[common_genes, ]
  45. pseudo_common <- pseudo_counts[common_genes, ]
  47. # ─────────────────────────────────────────────────────────────
  48. # 5. Define sample conditions
  49. # ─────────────────────────────────────────────────────────────
  50. conditions <- c(rep("Control", 8), rep("Incipient", 7), rep("Moderate", 8), rep("Severe", 7))
  51. sample_ids <- colnames(pseudo_counts)
  53. stopifnot(length(sample_ids) == length(conditions))
  55. sample_condition <- data.frame(Condition = conditions)
  56. rownames(sample_condition) <- sample_ids
  58. # ─────────────────────────────────────────────────────────────
  59. # 6. Run CDSeq per condition for ASTROCYTES
  60. # ─────────────────────────────────────────────────────────────
  61. pseudo_counts_t <- t(pseudo_counts)
  62. pseudo_df <- as.data.frame(pseudo_counts_t)
  63. pseudo_df$Condition <- sample_condition[rownames(pseudo_df), "Condition"]
  65. cdseq_results_astro <- list()
  67. for (cond in unique(sample_condition$Condition)) {
  68. message("Running CDSeq for condition: ", cond)
  70. subset_data <- subset(pseudo_df, Condition == cond)
  71. subset_data$Condition <- NULL
  72. expr_matrix <- t(subset_data)
  74. cdseq_results_astro[[cond]] <- CDSeq(
  75. bulk_data = expr_matrix,
  76. cell_type_number = 6,
  77. mcmc_iterations = 500
  78. )
  79. }
  81. save(cdseq_results_astro, file = "cdseq_results_astro.RData")
  83. # ─────────────────────────────────────────────────────────────
  84. # 7. Export estimated GEPs
  85. # ─────────────────────────────────────────────────────────────
  86. dir.create("GEP_astrocytes", showWarnings = FALSE)
  88. for (cond in names(cdseq_results_astro)) {
  89. write.csv(cdseq_results_astro[[cond]]$estGEP,
  90. file = file.path("GEP_astrocytes", paste0("GEP_astro_", cond, ".csv")),
  91. row.names = TRUE)
  92. }
  94. # ─────────────────────────────────────────────────────────────
  95. # 8. Select most astrocyte-like cell type per condition
  96. # ─────────────────────────────────────────────────────────────
  97. astro_profile <- ref_common[, "astrocyte", drop = FALSE]
  98. astro_profile <- astro_profile[order(rownames(astro_profile)), , drop = FALSE]
  100. best_geps <- list()
  102. for (cond in names(cdseq_results_astro)) {
  103. est_gep <- cdseq_results_astro[[cond]]$estGEP
  104. est_gep <- est_gep[order(rownames(est_gep)), ]
  106. shared_genes <- intersect(rownames(astro_profile), rownames(est_gep))
  108. correlations <- apply(est_gep[shared_genes, ], 2, function(x) {
  109. cor(x, astro_profile[shared_genes, "astrocyte"], method = "pearson")
  110. })
  112. best_index <- which.max(correlations)
  113. best_geps[[cond]] <- est_gep[, best_index, drop = FALSE]
  114. colnames(best_geps[[cond]]) <- cond
  115. }
  117. gep_best_astro <- do.call(cbind, best_geps)
  119. # ─────────────────────────────────────────────────────────────
  120. # 9. Load astrocyte marker genes (from your file)
  121. # ─────────────────────────────────────────────────────────────
  122. markers_df <- read_excel("astrocytesmarkers (1).xlsx")
  123. marker_genes <- unique(markers_df$Gene)
  125. # ─────────────────────────────────────────────────────────────
  126. # 10. Subset for marker genes and generate heatmap
  127. # ─────────────────────────────────────────────────────────────
  128. gep_filtered <- gep_best_astro[rownames(gep_best_astro) %in% marker_genes, ]
  129. gep_scaled <- t(scale(t(gep_filtered))) # z-score
  131. pheatmap(gep_scaled,
  132. cluster_rows = TRUE,
  133. cluster_cols = TRUE,
  134. color = colorRampPalette(c("blue", "white", "red"))(100),
  135. main = "Astrocyte Marker Genes (CDSeq GEPs)",
  136. fontsize_row = 7,
  137. fontsize_col = 12)

Deconvolution.R at commit 52a319e, no license · at the source

Overview

Authors: Andrea Angarita-Rodríguez1,2,3, Viviana Vargas-López1, Andrés Pinzón2, Adrián Sandoval-Hernandez4,5, Kai Kang6, Leping Li3, Jason Papin7,8,9, Pedro Puentes-Rozo10,11, Andrés Felipe Aristizábal1, Janneth González1
ORCID iDs: Viviana Vargas-López
  1. Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
  2. Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
  3. Biostatistics and Computational Biology Branch at NIEHS, Bethesda, MD, United States
  4. Grupo de Neurociencias y Muerte Celular, Instituto de Genética, Universidad Nacional de Colombia, Bogotá, Colombia
  5. Departamento de Química, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
  6. Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC, United States
  7. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
  8. Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, United States
  9. Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States
  10. Grupo de Neurociencias del Caribe, Unidad de Neurociencias Cognitivas, Universidad Simón Bolívar, Barranquilla, Colombia
  11. Grupo de Neurociencias del Caribe, Universidad del Atlántico, Barranquilla, Colombia
Institutions: Pontificia Universidad Javeriana (Colombia); Universidad Nacional de Colombia (Colombia); National Institute of Environmental Health Sciences (United States); University of North Carolina Wilmington (United States); University of Virginia (United States); University of Atlántico (Colombia); Universidad del Atlántico (Mexico); Universidad Simón Bolívar (Colombia); Simón Bolívar University (Venezuela)
Journal: Frontiers in bioinformatics, volume 6, article 1816121
Dates: received 23 February 2026; accepted 20 April 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fbinf.2026.1816121 · PMID 42311824 · PMCID PMC13269292 · OpenAlex W7163137584
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Connectivity
Keywords: genome-scale metabolic models, flux balance analysis, metabolic reprogramming, transcriptome, deconvolution, mild cognitive impairment, Alzheimer’s disease, astrocyte
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 92 references in the paper
Notices: A correction to this paper has been published (42643565, from Europe PMC)

Abstract

Introduction: Astrocytes are essential for maintaining neuronal homeostasis, yet their stage-specific contribution to mild cognitive impairment (MCI) and Alzheimer’s disease (AD) remains insufficiently understood. This study aimed to investigate astrocyte-associated transcriptional and metabolic alterations across the control-MCI-AD continuum using integrated transcriptomic and genome-scale metabolic modeling approaches.

Methods: Transcriptomic profiles from hippocampal CA1 tissue (GSE28146) were analyzed across four clinical conditions (control, early MCI, advanced MCI, and AD). Astrocyte-associated expression programs were inferred using the unsupervised deconvolution algorithm CDSeq and validated through canonical marker enrichment, correlation with external reference signatures, and comparison with an independent single-nucleus RNA-seq astrocyte pseudobulk dataset. The inferred profiles were integrated into a curated human astrocyte genome-scale metabolic model to generate condition-specific models, which were analyzed using flux balance analysis (FBA) and flux variability analysis (FVA).

Results: The analyses supported stage-dependent remodeling of astrocyte-associated transcriptional and metabolic programs during disease progression. Early MCI was associated with signaling and stress-adaptation changes, whereas advanced MCI and AD showed broader disruption of synaptic support, redox homeostasis, and inflammatory-related programs. Model predictions indicated a progressive reduction in a biomass-derived maintenance proxy from control to advanced MCI, followed by a partial rebound in AD, suggesting a compensatory shift toward reactive-like astrocyte states rather than full functional recovery. Flux variability analysis revealed reduced metabolic flexibility across disease stages, particularly in glutamate-glutamine cycling, glutathione/redox metabolism, glycolysis-pyruvate metabolism, cholesterol handling, and one-carbon/folate metabolism.

Discussion: These findings support the view that astrocytes undergo progressive, stage-specific metabolic reprogramming during the transition from healthy aging to AD. Early alterations in redox regulation, neurotransmitter cycling, and mitochondrial function may contribute to early neuronal vulnerability. This work highlights astrocyte-centered pathways as potential targets for future experimental validation and therapeutic exploration.

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 1 match between paragraphs and lines of code.

mangaritar/Astrocyte-metabolic-modeling-MCI-AD

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 52a319e33ba8c6345a4444ac94126d33a35e592c, 24 March 2026
Languages: MATLAB (6), R (2)
Size: 26 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), ggplot2 (1 file), pheatmap (1 file), reshape2 (1 file), reticulate (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/mangaritar/Astrocyte-metabolic-modeling-MCI-AD

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;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match 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 statement

Publicly available datasets were analyzed in this study. This data can be found here: Transcriptomic data analyzed in this study are publicly available in the Gene Expression Omnibus (GEO) under accession number GSE28146 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28146) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc&equals;GSE28146). The reconstructed metabolic models and analysis scripts are openly available at: https://github.com/mangaritar/Astrocyte-metabolic-modeling-MCI-AD.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 8 keywords, 92 references, 1 integrity notice.

Cite

This paper

Angarita-Rodríguez, A., Vargas-López, V., Pinzón, A., Sandoval-Hernandez, A., Kang, K., Li, L., Papin, J., Puentes-Rozo, P., Aristizábal, A. F., & González, J. (2026). Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression. Frontiers in bioinformatics, 6, 1816121. https://doi.org/10.3389/fbinf.2026.1816121

BibTeX

@article{angaritarodriguez2026transcriptome,
author = {Angarita-Rodríguez, Andrea and Vargas-López, Viviana and Pinzón, Andrés and Sandoval-Hernandez, Adrián and Kang, Kai and Li, Leping and Papin, Jason and Puentes-Rozo, Pedro and Aristizábal, Andrés Felipe and González, Janneth},
title = {{Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression}},
journal = {Frontiers in bioinformatics},
year = {2026},
month = jun,
volume = {6},
pages = {1816121},
publisher = {Frontiers Media SA},
issn = {2673-7647},
doi = {10.3389/fbinf.2026.1816121},
url = {https://doi.org/10.3389/fbinf.2026.1816121},
pmid = {42311824},
pmcid = {PMC13269292}
}

RIS

TY - JOUR
AU - Angarita-Rodríguez, Andrea
AU - Vargas-López, Viviana
AU - Pinzón, Andrés
AU - Sandoval-Hernandez, Adrián
AU - Kang, Kai
AU - Li, Leping
AU - Papin, Jason
AU - Puentes-Rozo, Pedro
AU - Aristizábal, Andrés Felipe
AU - González, Janneth
TI - Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression
T2 - Frontiers in bioinformatics
J2 - Front Bioinform
PY - 2026
DA - 2026/06/02
VL - 6
SP - 1816121
SN - 2673-7647
PB - Frontiers Media SA
DO - 10.3389/fbinf.2026.1816121
UR - https://doi.org/10.3389/fbinf.2026.1816121
LA - en
ER -

CSL-JSON

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