A cell surface interaction network of neural leucine-rich repeat receptors

神经亮氨酸富集重复受体的细胞表面相互作用网络

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作者:Christian Söllner, Gavin J Wright

Background

The vast number of precise intercellular connections within vertebrate nervous systems is only partly explained by the comparatively few known extracellular guidance cues. Large families of neural orphan receptor proteins have been identified and are likely to contribute to these recognition processes but due to the technical difficulty in identifying novel extracellular interactions of membrane-embedded proteins, their ligands remain unknown.

Conclusions

This integrated interaction and expression network provides a rich source of novel neural recognition pathways and highlights the importance of quantitative systematic extracellular protein interaction screens to mechanistically explain neural wiring patterns.

Results

To identify novel neural recognition signals, we performed a large systematic protein interaction screen using an assay capable of detecting low affinity extracellular protein interactions between the ectodomains of 150 zebrafish receptor proteins containing leucine-rich-repeat and/or immunoglobulin superfamily domains. We screened 7,592 interactions to construct a network of 34 cell surface receptor-ligand pairs that included orphan receptor subfamilies such as the Lrrtms, Lrrns and Elfns but also novel ligands for known receptors such as Robos and Unc5b. A quantitative biochemical analysis of a subnetwork involving the Unc5b and three Flrt receptors revealed a surprising quantitative variation in receptor binding strengths. Paired spatiotemporal gene expression patterns revealed dynamic neural receptor recognition maps within the developing nervous system, providing biological support for the network and revealing likely functions. Conclusions: This integrated interaction and expression network provides a rich source of novel neural recognition pathways and highlights the importance of quantitative systematic extracellular protein interaction screens to mechanistically explain neural wiring patterns.

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