Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 5, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2013.00022
Keywords
Huntington's disease; cellular-stress response; FOXO network; clinical potential; gene prioritization; system-level approach; entropy
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Funding
- Agence Nationale de la Recherche et de la Technologie (ANRT)
- Glaxo-Smith-Klyne, France
- CHDI Foundation(USA)
- Inserm
- Agence Nationale de la Recherche, Paris, France [ANR 08-MNPS-024-01]
- European Huntington Disease Network (EHDN, Germany)
- Hereditary Disease Foundation (USA)
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The FOXO family of transcription factors is central to the regulation of organismal longevity and cellular survival. Several studies have indicated that FOXO factors lie at the center of a complex network of upstream pathways, cofactors and downstream targets (FOXO-interaction networks), which may have developmental and post-developmental roles in the regulation of chronic-stress response in normal and diseased cells. Noticeably, FOXO factors are important for the regulation of proteotoxicity and neuron survival in several models of neurodegenerative disease, suggesting that FOXO-interaction networks may have therapeutic potential. However, the status of FOXO-interaction networks in neurodegenerative disease remains largely unknown. Systems modeling is anticipated to provide a comprehensive assessment of this question. In particular, interrogating the context-dependent variability of FOXO-interaction networks could predict the clinical potential of cellular-stress response genes and aging regulators for tackling brain and peripheral pathology in neurodegenerative disease. Using published transcriptomic data obtained from murine models of Huntington's disease (HD) and post-mortem brains, blood samples and induced-pluripotent-stem cells from HD carriers as a case example, this review briefly highlights how the biological status and clinical potential of FOXO-interaction networks for HD may be decoded by developing network and entropy based feature selection across heterogeneous datasets.
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