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Bioinformatics. 2019 Jun 22. pii: btz514. doi: 10.1093/bioinformatics/btz514. [Epub ahead of print]

Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntington's disease mice synchronous to symptoms.

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Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.
CHDI Foundation, Princeton, NJ, USA.



Huntington's disease (HD) may evolve through gene deregulation. However, the impact of gene deregulation on the dynamics of genetic cooperativity in HD remains poorly-understood. Here, we built a multi-layer network model of temporal dynamics of genetic cooperativity in the brain of HD knock-in mice (allelic series of Hdh mice). To enhance biological precision and gene prioritization, we integrated three complementary families of source networks, all inferred from the same RNA-seq time series data in Hdh mice, into weighted-edge networks where an edge recapitulates path-length variation across source-networks and age-points.


Weighted edge networks identify two consecutive waves of tight genetic cooperativity enriched in deregulated genes (critical phases), pre-symptomatically in the cortex, implicating neurotransmission, and symptomatically in the striatum, implicating cell survival (e.g. Hipk4) intertwined with cell proliferation (e.g. Scn4b) and cellular senescence (e.g. Cdkn2a products) responses. Top striatal weighted edges are enriched in modulators of defective behavior in invertebrate models of HD pathogenesis, validating their relevance to neuronal dysfunction in vivo. Collectively, these findings reveal highly-dynamic temporal features of genetic cooperativity in the brain of Hdh mice where a 2-step logic highlights the importance of cellular maintenance and senescence in the striatum of symptomatic mice, providing highly-prioritized targets.


Weighted edge network analysis (WENA) data and source codes for performing spectral decomposition of the signal (SDS) and WENA analysis, both written using Python, are available at


Supplementary data are available at Bioinformatics online.

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