A personalized metabolic modelling approach through integrated analysis of RNA-Seq-based genomic variants and gene expression levels in Alzheimer’s disease
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Abstract
Abstract Motivation Alzheimer’s disease (AD) is known to cause alterations in brain metabolism. Furthermore, genomic variants in enzyme-coding genes may exacerbate AD-linked metabolic changes. Generating condition-specific metabolic models by mapping gene expression data to genome-scale metabolic models is a routine approach to elucidate disease mechanisms from a metabolic perspective. RNAseq data provides both gene expression and genomic variation information. Integrating variants that perturb enzyme functionality from the same RNAseq data may enhance model accuracy, offering insights into genome-wide AD metabolic pathology. Results Our study pioneers the extraction of both transcriptomic and genomic data from the same RNA-seq data to reconstruct personalized metabolic models. We mapped genes with significantly higher load of pathogenic variants in AD onto a human genome-scale metabolic network together with the gene expression data. Comparative analysis of the resulting personalized patient metabolic models with the control models showed enhanced accuracy in detecting AD-associated metabolic pathways compared to the case where only expression data was mapped on the metabolic network. Besides, several otherwise would-be missed pathways were annotated in AD by considering the effect of genomic variants. Implementation The scripts are available at https://github.com/SysBioGTU/GenomicVariantsMetabolicModels . Contact tcakir@gtu.edu.tr
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AI-driven approaches have enabled more comprehensive analyses of mitochondrial gene expression profiles and metabolic pathway alterations.
“AI-driven approaches have enabled more comprehensive analyses of mitochondrial gene expression profiles and metabolic pathway alterations47,48.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [48]
In Results and discussion, under “Differentially perturbed pathways” (page not specified), the paper reports that models integrating genomic variants with gene-expression data identified more altered metabolic pathways than expression-only models. It does not establish that the approach was AI-driven or that it provided a more comprehensive analysis of mitochondrial gene-expression profiles. (LLM, conf=0.84)
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Self-citation
- Authors
- Uzuner Odongo, D., İlgün, A., Bozkurt, F. B., & Çakır, T.
- Affiliations
- Gebze Technical University (TR)
- Corresponding author
- Çakır, Tunahan
- Venue
- Communications Biology
- Published
- 2025-03-27
- Volume
- 8
- Issue
- 1
- Pages
- 502-502
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC11950204
- OpenAlex
- W4408912420
- Cited by
- 4
- References
- 63
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Communications Biology (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (100/2000, 120S824)
- Yükseköğretim Kurulu (100/ 2000)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Alzheimer's disease, genome-scale metabolic models, RNA-seq, genetic variants, gene expression, pathogenic variants, metabolic pathways
- MeSH terms
- RNA-Seq, Alzheimer Disease, Humans, Models, Biological, Genetic Variation, Gene Expression Profiling, Genomics, Metabolic Networks and Pathways, Precision Medicine, Transcriptome
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