ArticlePloS one2014
Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 65 citations in OpenAlex.
- Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants.iScience · 2026Article
- APOE genotype influences on the brain metabolome of aging mice - role for mitochondrial energetics in mechanisms of resilience in APOE2 genotype.Molecular neurodegeneration · 2025Article
- The aggregate proteome ofFrontiers in aging neuroscience · 2025Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
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- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling.Scientific reports · 2024Article
- Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries.Advances in neurobiology · 2024Review
- A Comprehensive NMR Analysis of Serum and Fecal Metabolites in Familial Dysautonomia Patients Reveals Significant Metabolic Perturbations.Metabolites · 2023Article
- Integrating proteomic data with metabolic modeling provides insight into key pathways ofFrontiers in microbiology · 2023Article
- Systemic alterations of tricarboxylic acid cycle enzymes in Alzheimer's disease.Frontiers in neuroscience · 2023Article
- Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis.BMC bioinformatics · 2022Article
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- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
- Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation.Journal of personalized medicine · 2022Review
- Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis.International journal of molecular sciences · 2021Article
- The Role of the Rare Variants in the Genes Encoding the Alpha-Ketoglutarate Dehydrogenase in Alzheimer's Disease.Life (Basel, Switzerland) · 2021Article
- Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.Toxicology and applied pharmacology · 2021Article
- Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer's Disease.Aging and disease · 2020Article
- Towards the routine use of in silico screenings for drug discovery using metabolic modelling.Biochemical Society transactions · 2020Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment.
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