Physiological Consequences of Targeting 14-3-3 and Its Interacting Partners in Neurodegenerative Diseases

The mammalian 14-3-3 family comprises seven intrinsically unstructured, evolutionarily conserved proteins that bind >200 protein targets, thereby modulating cell-signaling pathways. The presence of 14-3-3 proteins in cerebrospinal fluid provides a sensitive and specific biomarker of neuronal damage associated with Alzheimer's disease (AD), Creutzfeldt-Jakob disease (CJD), spongiform encephalitis, brain cancers, and stroke. We observed significant enrichment of 14-3-3 paralogs G, S, and Z in human brain aggregates diagnostic of AD. We used intra-aggregate crosslinking to identify 14-3-3 interaction partners, all of which were significantly enriched in AD brain aggregates relative to controls. We screened FDA-approved drugs in silico for structures that could target the 14-3-3G/hexokinase interface, an interaction specific to aggregates and AD. C. elegans possesses only two 14-3-3 orthologs, which bind diverse proteins including DAF-16 (a FOXO transcription factor) and SIR-2.1 (a sensor of nutrients and stress), influencing lifespan. Top drug candidates were tested in C. elegans models of neurodegeneration-associated aggregation and in a human neuroblastoma cell-culture model of AD-like amyloidosis. Several drugs opposed aggregation in all models assessed and rescued behavioral deficits in C. elegans AD-like neuropathy models, suggesting that 14-3-3 proteins are instrumental in aggregate accrual and supporting the advancement of drugs targeting 14-3-3 protein complexes with their partners.

Automated summary

The presence of 14-3-3 proteins in cerebrospinal fluid can serve as a specific biomarker for neuronal damage-related diseases. In this study, the enrichment of 14-3-3 proteins in brain aggregates associated with Alzheimer's disease was observed. Interaction partners of 14-3-3 proteins were identified, and drugs targeting the protein complexes showed potential in inhibiting aggregation and rescuing behavioral deficits in disease models.