Computational Approaches to Assess Abnormal Metabolism in Alzheimer’s Disease Using Transcriptomics

Lüleci, Hatice Büşra; Uzuner, Dilara; Çakır, Tunahan; Thambisetty, Madhav

doi:10.1007/978-1-0716-2655-9_9

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reconstruct personalized genome-scale metabolic models for all controls and AD cases, genes in the covariate-adjusted expression data were mapped individually to the human genome-scale metabolic model (Human-GEM) using the integrative metabolic analysis tool (iMAT) (Zur, et al, 2010). The iMAT algorithm was run with the parameters described in our previous study (Lüleci, et al, 2023). Due to the covariate adjustment of the expression data, there are negative values in the data.…”

Section: Personalized Metabolic Model Reconstructionmentioning

confidence: 99%

A Personalized Metabolic Modelling Approach through Integrated Analysis of RNA-Seq-Based Genomic Variants and Gene Expression Levels in Alzheimer’s Disease

Uzuner,

İlgün,

Bozkurt

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Personalized Metabolic Model Reconstructionmentioning

confidence: 99%