Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling
Abstract 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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-51034-y |
| _version_ | 1797363296549994496 |
|---|---|
| author | Ecehan Abdik Tunahan Çakır |
| author_facet | Ecehan Abdik Tunahan Çakır |
| author_sort | Ecehan Abdik |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T16:19:19Z |
| format | Article |
| id | doaj.art-535dcdf513634240a41d426586e69cdf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T16:19:19Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-535dcdf513634240a41d426586e69cdf2024-01-07T12:26:57ZengNature PortfolioScientific Reports2045-23222024-01-0114111510.1038/s41598-023-51034-yTranscriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modelingEcehan Abdik0Tunahan Çakır1Department of Bioengineering, Gebze Technical UniversityDepartment of Bioengineering, Gebze Technical UniversityAbstract 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.https://doi.org/10.1038/s41598-023-51034-y |
| spellingShingle | Ecehan Abdik Tunahan Çakır Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling Scientific Reports |
| title | Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling |
| title_full | Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling |
| title_fullStr | Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling |
| title_full_unstemmed | Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling |
| title_short | Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling |
| title_sort | transcriptome based biomarker prediction for parkinson s disease using genome scale metabolic modeling |
| url | https://doi.org/10.1038/s41598-023-51034-y |
| work_keys_str_mv | AT ecehanabdik transcriptomebasedbiomarkerpredictionforparkinsonsdiseaseusinggenomescalemetabolicmodeling AT tunahancakır transcriptomebasedbiomarkerpredictionforparkinsonsdiseaseusinggenomescalemetabolicmodeling |