The assembly of specific neuronal circuits relies on the expression of complementary molecular programs in presynaptic and postsynaptic neurons. In the cerebral cortex, the tyrosine kinase receptor ErbB4 is critical for the wiring of specific populations of GABAergic interneurons, in which it paradoxically regulates both the formation of inhibitory synapses as well as the development of excitatory synapses received by these cells. Here we found that Nrg1 and Nrg3, two members of the neuregulin family of trophic factors, respectively regulate the inhibitory outputs and excitatory inputs of interneurons in the mouse cerebral cortex. The differential role of Nrg1 and Nrg3 in this process is not due to their receptor-binding EGF-like domain, but rather to their distinctive subcellular localization within pyramidal cells. Our study reveals a novel strategy for the assembly of cortical circuits that involves the differential subcellular sorting of family-related synaptic proteins.
All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for the figures and figure supplements.
- Oscar Marín
- Beatriz Rico
- Oscar Marín
- Beatriz Rico
- Oscar Marín
- David Exposito-Alonso
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals in accordance with European regulations, and Home Office personal and project licenses (PPL 0808-2004-2019, PPL PD025E9BC-2019-2024) under the UK Animals (Scientific Procedures) 1986 Act. The experiments performed in this study, have been designed to follow the 3R's rules whenever possible.
- Nils Brose, Max Planck Institute of Experimental Medicine, Germany
© 2020, Exposito-Alonso et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The amyloid beta (Aβ) plaques found in Alzheimer’s disease (AD) patients’ brains contain collagens and are embedded extracellularly. Several collagens have been proposed to influence Aβ aggregate formation, yet their role in clearance is unknown. To investigate the potential role of collagens in forming and clearance of extracellular aggregates in vivo, we created a transgenic Caenorhabditis elegans strain that expresses and secretes human Aβ1-42. This secreted Aβ forms aggregates in two distinct places within the extracellular matrix. In a screen for extracellular human Aβ aggregation regulators, we identified different collagens to ameliorate or potentiate Aβ aggregation. We show that a disintegrin and metalloprotease a disintegrin and metalloprotease 2 (ADM-2), an ortholog of ADAM9, reduces the load of extracellular Aβ aggregates. ADM-2 is required and sufficient to remove the extracellular Aβ aggregates. Thus, we provide in vivo evidence of collagens essential for aggregate formation and metalloprotease participating in extracellular Aβ aggregate removal.
The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.