Amyloid-β precursor protein (APP) regulates neuronal activity through the release of secreted APP (sAPP) acting at cell-surface receptors. APP and sAPP were reported to bind to the extracellular sushi domain 1 (SD1) of GABAB receptors (GBRs). A 17 amino-acid peptide (APP17) derived from APP was sufficient for SD1 binding and shown to mimic the inhibitory effect of sAPP on neurotransmitter release and neuronal activity. The functional effects of APP17 and sAPP were similar to those of the GBR agonist baclofen and blocked by a GBR antagonist. These experiments led to the proposal that sAPP activates GBRs to exert its neuronal effects. However, whether APP17 and sAPP influence classical GBR signaling pathways in heterologous cells was not analyzed. Here, we confirm that APP17 binds to GBRs with nanomolar affinity. However, biochemical and electrophysiological experiments indicate that APP17 does not influence GBR activity in heterologous cells. Moreover, APP17 did not regulate synaptic GBR localization, GBR-activated K+ currents, neurotransmitter release or neuronal activity in vitro or in vivo. Our results show that APP17 is not a functional GBR ligand and indicate that sAPP exerts its neuronal effects through receptors other than GBRs.
For all figures, numerical data that are represented in graphs are provided as source data excel files.
- Bernhard Bettler
- Sebastian Reinartz
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All animal experiments were approved by the veterinary office of the canton of Basel-Stadt, Switzerland (animal license numbers: 1897_31476 and 3004_34045).
- Moses V Chao, New York University Langone Medical Center, United States
© 2023, Rem 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.