A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease
Abstract Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85580-4 |
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| author | Ozan Ozisik Nazli Sila Kara Tooba Abbassi-Daloii Morgane Térézol Elsa C. Kuijper Núria Queralt-Rosinach Annika Jacobsen Osman Ugur Sezerman Marco Roos Chris T. Evelo Anaïs Baudot Friederike Ehrhart Eleni Mina |
| author_facet | Ozan Ozisik Nazli Sila Kara Tooba Abbassi-Daloii Morgane Térézol Elsa C. Kuijper Núria Queralt-Rosinach Annika Jacobsen Osman Ugur Sezerman Marco Roos Chris T. Evelo Anaïs Baudot Friederike Ehrhart Eleni Mina |
| author_sort | Ozan Ozisik |
| collection | DOAJ |
| description | Abstract Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives. We analyzed a Huntington’s disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington’s disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method. |
| format | Article |
| id | doaj-art-3777e78771024510bb18a6b0c4368fa6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3777e78771024510bb18a6b0c4368fa62025-01-12T12:22:17ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-85580-4A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s diseaseOzan Ozisik0Nazli Sila Kara1Tooba Abbassi-Daloii2Morgane Térézol3Elsa C. Kuijper4Núria Queralt-Rosinach5Annika Jacobsen6Osman Ugur Sezerman7Marco Roos8Chris T. Evelo9Anaïs Baudot10Friederike Ehrhart11Eleni Mina12Aix Marseille Univ, INSERM, MMGInstitute of Biology and Medical Genetics, First Faculty of Medicine, Charles UniversityDepartment of Human Genetics, Leiden University Medical CenterAix Marseille Univ, INSERM, MMGDepartment of Human Genetics, Leiden University Medical CenterDepartment of Human Genetics, Leiden University Medical CenterDepartment of Human Genetics, Leiden University Medical CenterDepartment of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar UniversityDepartment of Human Genetics, Leiden University Medical CenterDepartment of Bioinformatics-BiGCaT, NUTRIM/MHeNs, Maastricht UniversityAix Marseille Univ, INSERM, MMGDepartment of Bioinformatics-BiGCaT, NUTRIM/MHeNs, Maastricht UniversityDepartment of Human Genetics, Leiden University Medical CenterAbstract Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives. We analyzed a Huntington’s disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington’s disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.https://doi.org/10.1038/s41598-025-85580-4Huntington’s diseaseRare diseaseNetwork analysisCollaborative analysis |
| spellingShingle | Ozan Ozisik Nazli Sila Kara Tooba Abbassi-Daloii Morgane Térézol Elsa C. Kuijper Núria Queralt-Rosinach Annika Jacobsen Osman Ugur Sezerman Marco Roos Chris T. Evelo Anaïs Baudot Friederike Ehrhart Eleni Mina A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease Scientific Reports Huntington’s disease Rare disease Network analysis Collaborative analysis |
| title | A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease |
| title_full | A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease |
| title_fullStr | A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease |
| title_full_unstemmed | A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease |
| title_short | A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease |
| title_sort | collaborative network analysis for the interpretation of transcriptomics data in huntington s disease |
| topic | Huntington’s disease Rare disease Network analysis Collaborative analysis |
| url | https://doi.org/10.1038/s41598-025-85580-4 |
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