RESUMEN
The GluA2 subunit of AMPA-type glutamate receptors (AMPARs) regulates excitatory synaptic transmission in neurons. In addition, the transsynaptic cell adhesion molecule N-cadherin controls excitatory synapse function and stabilizes dendritic spine structures. At postsynaptic membranes, GluA2 physically binds N-cadherin, underlying spine growth and synaptic modulation. We report that N-cadherin binds to PSD-95/SAP90/DLG/ZO-1 (PDZ) domain 2 of the glutamate receptor interacting protein 1 (GRIP1) through its intracellular C terminus. N-cadherin and GluA2-containing AMPARs are presorted to identical transport vesicles for dendrite delivery, and live imaging reveals cotransport of both proteins. The kinesin KIF5 powers GluA2/N-cadherin codelivery by using GRIP1 as a multilink interface. Notably, GluA2 and N-cadherin use different PDZ domains on GRIP1 to simultaneously bind the transport complex, and interference with either binding motif impairs the turnover of both synaptic cargoes. Depolymerization of microtubules, deletion of the KIF5 motor domain, or specific blockade of AMPAR exocytosis affects delivery of GluA2/N-cadherin vesicles. At the functional level, interference with this cotransport reduces the number of spine protrusions and excitatory synapses. Our data suggest the concept that the multi-PDZ-domain adaptor protein GRIP1 can act as a scaffold at trafficking vesicles in the combined delivery of AMPARs and N-cadherin into dendrites.
Asunto(s)
Cadherinas/metabolismo , Dendritas/metabolismo , Proteínas del Tejido Nervioso/metabolismo , Receptores AMPA/metabolismo , Vesículas Transportadoras/metabolismo , Animales , Dendritas/ultraestructura , Células HEK293 , Humanos , Cinesinas/metabolismo , Ratones , Unión Proteica , Transporte de Proteínas , Ratas , Sinapsis/metabolismo , Sinapsis/ultraestructuraRESUMEN
A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.