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Integrated Multi-Tissue Transcriptomics Reveals Antagonistic Pleiotropy in Aging and Alzheimer's Disease.

Code ↔ Paper

18 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 18 matches
  1. [1] § Methods › WGCNA analysis and AP module/gene scoring ↔ 10_modules_AP.R, lines 316–342 · score 0.87 · TOMType, mergeCutHeight, minModuleSize, soft thresholding power, networkType, WGCNA
  2. [2] § Methods › Functional enrichment of AP-derived gene sets ↔ 09_go_kegg_enrichment.R, lines 67–121 · score 0.81 · clusterProfiler, p.adjust, hsa, simplify, CC, MF
  3. [3] § Methods › Cell-state enrichment analysis and cell type-adjusted differential expression ↔ 07_optional_gsva_deconv.R, lines 209–229 · score 0.81 · C8 brain cell, Brain cell state, neuron, pericyte, endothelial, excitatory
  4. [4] § Methods › Functional enrichment of AP-derived gene sets ↔ withDevelopment_section.R, lines 1318–1444 · score 0.81 · Entrez IDs, clusterProfiler, hsa, CC, MF, BP
  5. [5] § Methods › Pathway and TF analyses ↔ 05_tf_activity.R, lines 290–345 · score 0.81 · TF activities, AP overlap, AP weighted, DoRothEA, TFs, regulons
  6. [6] § Methods › WGCNA analysis and AP module/gene scoring ↔ 10_modules_AP.R, lines 316–342 · score 0.79 · merge cut height, soft thresholding power, module AP, module gene, WGCNA, network
  7. [7] § Methods › Gene classification, prioritization, and AP metrics ↔ 02_consensus_meta.R, lines 89–158 · score 0.75 · divergence score, discordant Age, shared score, log2FC, log10, consensus
  8. [8] § Methods › Pathway and TF analyses ↔ 03_pathway_antagonism.R, lines 16–39 · score 0.69 · fgseaMultilevel, fgseaSimple, permutations, NES, Pathway, antagonism
  9. [9] § Methods › WGCNA analysis and AP module/gene scoring ↔ 10_modules_AP.R, lines 807–845 · score 0.66 · ego networks, scoring genes, AP class, AP scores, hub, exported
  10. [10] § Methods › Covariate and brain-only sensitivity audits ↔ 04_ap_candidates.R, lines 665–718 · score 0.60 · AD brain meta, aging meta, AP candidates, filtered, prioritization, class
  11. [11] § Methods › Cross-condition integration and meta-analysis ↔ 01_prepare_universe.R, lines 71–122 · score 0.58 · HGNC symbols, gene symbol, log2FC, Ensembl
  12. [12] § Methods › Gene classification, prioritization, and AP metrics ↔ 02_consensus_meta.R, lines 89–158 · score 0.58 · Aging pleiotropy, aging contrasts, log2FC, consensus, prioritization, AD
  13. [13] § Methods › Cross-condition integration and meta-analysis ↔ 04_ap_candidates.R, lines 156–189 · score 0.57 · HGNC symbols, gene symbol, log2fc, Ensembl, AP
  14. [14] § Results › TF activity antagonism between healthy aging and AD ↔ 05_tf_activity.R, lines 290–345 · score 0.55 · TF activity, DoRothEA, regulons, NES, map, antagonism
  15. [15] § Results › Cell state-adjusted robustness of AD-associated AP signals ↔ 07_optional_gsva_deconv.R, lines 209–229 · score 0.53 · Cell state, endothelial, excitatory, astrocyte, inhibitory, oligodendrocyte
  16. [16] § Results › Integrated hallmark pathway across brain aging and AD ↔ utils.R, lines 109–117 · score 0.53 · SharedDown, SharedUp, AP Vulnerability, AP Resilience, quadrant, pathways
  17. [17] § Results › Integrated hallmark pathway across brain aging and AD ↔ 05_tf_activity.R, lines 147–155 · score 0.52 · SharedDown, SharedUp, AP Vulnerability, AP Resilience, quadrant, map
  18. [18] § Methods › WGCNA analysis and AP module/gene scoring ↔ 10_modules_AP.R, lines 1–60 · score 0.51 · aging samples, finite, young, GSE48350, WGCNA, Weighted

Paper

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

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The authors' code

R · 1,242 lines · 59 KB · no license · 4 matches

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It can be read at the source: 10_modules_AP.R.

Overview

Authors: Rana Salihoglu1, Şehnaz Can2, Thomas Dandekar1, Elena Bencurova1
ORCID iDs: Rana Salihoglu, Şehnaz Can, Thomas Dandekar, Elena Bencurova
  1. Department of Bioinformatics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
  2. Department of Bioengineering, Marmara University, Istanbul, Turkey
Institutions: University of Würzburg (Germany); Marmara University (Türkiye)
Journal: Computational and structural biotechnology journal, volume 35, issue 1, article 0134
Dates: received 30 March 2026; accepted 21 May 2026; published online 8 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.34133/csbj.0134 · PMID 42267139 · PMCID PMC13243799 · OpenAlex W7162071788
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, Preprocessing, Connectivity
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

Aging is the strongest risk factor for Alzheimer’s disease (AD); however, some individuals age without major cognitive decline, suggesting that resilience and vulnerability may be associated with distinct molecular trajectories. To investigate these trajectories, we performed an integrated transcriptomic analysis of human dermal fibroblasts (GSE113957) and multi-region brain profiles (GSE48350), extending previous dataset-specific studies that focused primarily on age prediction, regional variation, or synaptic/immune signatures. Healthy aging and AD were compared within a novel antagonistic pleiotropy (AP) framework. This approach prioritized genes and candidate transcriptional regulators with opposing age and disease-associated expression patterns. Across tissues, healthy aging was associated with relative preservation of metabolic, mitochondrial, and lipid-homeostatic programs, whereas AD was associated with suppression of these programs alongside greater inflammatory and immune pathway activity. AP-Vulnerability genes (Age↓/AD↑), including TAC1, FREM3, and SLC25A46, declined with age but were induced in AD. Conversely, AP-Resilience genes (Age↑/AD↓), including PTH2, PPDPF, and NEFH, increased during healthy aging but were reduced in AD. Pathway analyses suggested an association between metabolic programs and resilience, and between immune activation and vulnerability. Transcription-factor inference prioritized PPARG, NFE2L2, and TEAD4 as candidate resilience-associated regulators, showing directionally opposite patterns relative to immune- and developmental-related regulators in AD.

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 18 matches between paragraphs and lines of code.

salihoglu/Alzheimer_AP

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 377f6aed20f1984d011e84b99e5ed5cd2db0c811, 30 March 2026
Languages: R (14)
Size: 15 files, 14 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), ggplot2 (9 files), clusterProfiler (2 files), igraph (1 file), patchwork (1 file), pheatmap (1 file), SingleCellExperiment (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files, not copied: shown from their source

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At the source: github.com/salihoglu/Alzheimer_AP

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;
  • 14 scripts, each with its path and the digest of its content;
  • 18 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

https://github.com/salihoglu/Alzheimer_AP

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 88 references.

Cite

This paper

Salihoglu, R., Can, Ş., Dandekar, T., & Bencurova, E. (2026). Integrated Multi-Tissue Transcriptomics Reveals Antagonistic Pleiotropy in Aging and Alzheimer's Disease. Computational and structural biotechnology journal, 35(1), 0134. https://doi.org/10.34133/csbj.0134

BibTeX

@article{salihoglu2026integrated,
author = {Salihoglu, Rana and Can, Şehnaz and Dandekar, Thomas and Bencurova, Elena},
title = {{Integrated Multi-Tissue Transcriptomics Reveals Antagonistic Pleiotropy in Aging and Alzheimer's Disease}},
journal = {Computational and structural biotechnology journal},
year = {2026},
month = jun,
volume = {35},
number = {1},
pages = {0134},
publisher = {AAAS Science Partner Journal Program},
issn = {2001-0370},
doi = {10.34133/csbj.0134},
url = {https://doi.org/10.34133/csbj.0134},
pmid = {42267139},
pmcid = {PMC13243799}
}

RIS

TY - JOUR
AU - Salihoglu, Rana
AU - Can, Şehnaz
AU - Dandekar, Thomas
AU - Bencurova, Elena
TI - Integrated Multi-Tissue Transcriptomics Reveals Antagonistic Pleiotropy in Aging and Alzheimer's Disease
T2 - Computational and structural biotechnology journal
J2 - Comput Struct Biotechnol J
PY - 2026
DA - 2026/06/08
VL - 35
IS - 1
SP - 0134
SN - 2001-0370
PB - AAAS Science Partner Journal Program
DO - 10.34133/csbj.0134
UR - https://doi.org/10.34133/csbj.0134
LA - en
ER -

CSL-JSON

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