Ozisik Ozan, Kara Nazli Sila, Abbassi-Daloii Tooba, Térézol Morgane, Kuijper Elsa C, Queralt-Rosinach Núria, Jacobsen Annika, Sezerman Osman Ugur, Roos Marco, Evelo Chris T, Baudot Anaïs, Ehrhart Friederike, Mina Eleni
Aix Marseille Univ, INSERM, MMG, Marseille, France.
Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic.
Sci Rep. 2025 Jan 9;15(1):1412. doi: 10.1038/s41598-025-85580-4.
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.
罕见病可能会影响患者的生活质量并危及生命。治疗机会往往有限，部分原因是对这些疾病的分子机制缺乏了解。这可归因于罕见病的低发病率，因此可用于研究的样本量较少。克服这一问题的一种方法是使用基于网络的方法将实验性罕见病数据与先验知识相结合。更进一步，我们假设结合并分析多种基于网络的方法的结果可以从多个角度提供数据驱动的致病机制假设。我们以协作的方式使用六种基于网络的方法分析了亨廷顿病转录组数据集。这些方法要么本身报告了富集的注释术语，要么将其结果输入富集分析。然后使用本体层次结构对产生的显著富集的Reactome通路进行总结，这允许整合和解释多种方法的输出。在产生的富集通路中，有些通路先前已被证明与亨廷顿病有关，而有些通路对疾病发病机制的直接贡献仍不清楚，需要进一步研究。总之，我们的研究表明，协作网络分析方法非常适合研究罕见病，因为它们从多个角度提供致病机制的假设。对同一案例研究应用不同的方法可以揭示不同的疾病机制，而这些机制用单一方法应用时并不明显。
