Background As a growing and more diverse amount of genetic and molecular profile data are generated in several models of Huntington’s disease (HD), the potential of these datasets for enhancing HD research has significantly increased. Network-based analysis has the capacity to identify robust HD-associated rules and signatures from complex datasets. These rules and signatures can in turn be exploited for better understanding HD biology on a global scale as well as for prioritising models and genes around questions of specific interest, thereby supporting decision making and biomedical intelligence in HD research.

Aim and method To support biomedical intelligence in HD, and as part of the project and activities of the EHDN’s working group ‘Biological modifiers’, we have developed Biogemix, a network-based data integration framework for precise rule/pattern extraction across models and species. We used this framework for integrating greater than 16 publically available datasets including transcriptomic and gene perturbation data.

Outcome We found previously suspected as well as novel HD-associated features, the latter providing new insights into the global effects of mutant huntingtin pathogenicity. Additionally, we managed to determine the functional distances between experimental models of HD and assess the biological relevance of individual models of HD, functional profiles of biological modifiers and shared/unshared properties between pathological and compensatory/survival genes.

Conclusion Our results validate the value and precision of the Biogemix framework for actionable knowledge in HD. As larger HD datasets become available, our results suggest that applying the Biogemix approach to the analysis of these datasets will provide deep insights into the essential features of mutant huntingtin pathogenicity while providing strong guidance to translational and pharmacological research in HD.

Support CHDI Foundation, ANR, EHDN, ANRT/GSK.