Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a comput...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2024-01, Vol.14 (1), p.585-585, Article 585
Main Authors: Abdik, Ecehan, Çakır, Tunahan
Format: Article
Language:English
Subjects:
631/114
631/337
631/553
692/53
692/699/375/1718
Algorithms
Biomarkers
Disease
Genomes
Humanities and Social Sciences
Metabolism
Metabolites
Movement disorders
multidisciplinary
Neurodegenerative diseases
Parkinson's disease
Predictions
Science
Science (multidisciplinary)
Substantia nigra
Transcriptomes
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On–Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-51034-y