Heterogeneous Nuclear Ribonucleoprotein K Interacts with Abi-1 at Postsynaptic Sites and Modulates Dendritic Spine Morphology

BACKGROUND: Abelson-interacting protein 1 (Abi-1) plays an important role for dendritic branching and synapse formation in the central nervous system. It is localized at the postsynaptic density (PSD) and rapidly translocates to the nucleus upon synaptic stimulation. At PSDs Abi-1 is in a complex with several other proteins including WASP/WAVE or cortactin thereby regulating the actin cytoskeleton via the Arp 2/3 complex. PRINCIPAL FINDINGS: We identified heterogeneous nuclear ribonucleoprotein K (hnRNPK), a 65 kDa ssDNA/RNA-binding-protein that is involved in multiple intracellular signaling cascades, as a binding partner of Abi-1 at postsynaptic sites. The interaction with the Abi-1 SH3 domain is mediated by the hnRNPK-interaction (KI) domain. We further show that during brain development, hnRNPK expression becomes more and more restricted to granule cells of the cerebellum and hippocampal neurons where it localizes in the cell nucleus as well as in the spine/dendritic compartment. The downregulation of hnRNPK in cultured hippocampal neurons by RNAi results in an enlarged dendritic tree and a significant increase in filopodia formation. This is accompanied by a decrease in the number of mature synapses. Both effects therefore mimic the neuronal morphology after downregulation of Abi-1 mRNA in neurons. CONCLUSIONS: Our findings demonstrate a novel interplay between hnRNPK and Abi-1 in the nucleus and at synaptic sites and show obvious similarities regarding both protein knockdown phenotypes. This indicates that hnRNPK and Abi-1 act synergistic in a multiprotein complex that regulates the crucial balance between filopodia formation and synaptic maturation in neurons

The Function of Cortactin in the Clustering of Acetylcholine Receptors at the Vertebrate Neuromuscular Junction

Background: Postsynaptic enrichment of acetylcholine receptors (AChRs) at the vertebrate neuromuscular junction (NMJ) depends on the activation of the muscle receptor tyrosine MuSK by neural agrin. Agrin-stimulation of MuSK is known to initiate an intracellular signaling cascade that leads to the clustering of AChRs in an actin polymerization-dependent manner, but the molecular steps which link MuSK activation to AChR aggregation remain incompletely defined. Methodology/Principal Findings: In this study we used biochemical, cell biological and molecular assays to investigate a possible role in AChR clustering of cortactin, a protein which is a tyrosine kinase substrate and a regulator of F-actin assembly and which has also been previously localized at AChR clustering sites. We report that cortactin was co-enriched at AChR clusters in situ with its target the Arp2/3 complex, which is a key stimulator of actin polymerization in cells. Cortactin was further preferentially tyrosine phosphorylated at AChR clustering sites and treatment of myotubes with agrin significantly enhanced the tyrosine phosphorylation of cortactin. Importantly, forced expression in myotubes of a tyrosine phosphorylation-defective cortactin mutant (but not wild-type cortactin) suppressed agrin-dependent AChR clustering, as did the reduction of endogenous cortactin levels using RNA interference, and introduction of the mutant cortactin into muscle cells potently inhibited synaptic AChR aggregation in response to innervation. Conclusion: Our results suggest a novel function of phosphorylation-dependent cortactin signaling downstream fro

Theoretical Model for Cellular Shapes Driven by Protrusive and Adhesive Forces

The forces that arise from the actin cytoskeleton play a crucial role in determining the cell shape. These include protrusive forces due to actin polymerization and adhesion to the external matrix. We present here a theoretical model for the cellular shapes resulting from the feedback between the membrane shape and the forces acting on the membrane, mediated by curvature-sensitive membrane complexes of a convex shape. In previous theoretical studies we have investigated the regimes of linear instability where spontaneous formation of cellular protrusions is initiated. Here we calculate the evolution of a two dimensional cell contour beyond the linear regime and determine the final steady-state shapes arising within the model. We find that shapes driven by adhesion or by actin polymerization (lamellipodia) have very different morphologies, as observed in cells. Furthermore, we find that as the strength of the protrusive forces diminish, the system approaches a stabilization of a periodic pattern of protrusions. This result can provide an explanation for a number of puzzling experimental observations regarding cellular shape dependence on the properties of the extra-cellular matrix

Probabilistic prediction and ranking of human protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.</p> <p>Results</p> <p>The prediction of human protein-protein interactions was investigated by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. Using a conservative threshold, our most accurate predictor identifies 37606 human interactions, 32892 (80%) of which are not present in other publicly available large human interaction datasets, thus substantially increasing the coverage of the human interaction map. A subset of the 32892 novel predicted interactions have been independently validated. Comparison of the prediction dataset to other available human interaction datasets estimates the false positive rate of the new method to be below 80% which is competitive with other methods. Since the new method scores and ranks all human protein pairs, smaller subsets of higher quality can be generated thus leading to even lower false positive prediction rates.</p> <p>Conclusion</p> <p>The set of interactions predicted in this work increases the coverage of the human interaction map and will help determine the highest confidence human interactions.</p