Critical role for piccolo in synaptic vesicle retrieval
Abstract
Loss of function of the active zone protein Piccolo has recently been linked to a disease, Pontocerebellar Hypoplasia type 3, which causes brain atrophy. Here, we address how Piccolo inactivation in rat neurons adversely affects synaptic function and thus may contribute to neuronal loss. Our analysis shows that Piccolo is critical for the recycling and maintenance of synaptic vesicles. We find that boutons lacking Piccolo have deficits in the Rab5/EEA1 dependent formation of early endosomes and thus the recycling of SVs. Mechanistically, impaired Rab5 function was caused by reduced synaptic recruitment of Pra1, known to interact selectively with the zinc finger domains of Piccolo. Importantly, over-expression of GTPase deficient Rab5 or the Znf1 domain of Piccolo restores the size and recycling of SV pools. These data provide a molecular link between the active zone and endosome sorting at synapses providing hints to how Piccolo contributes to developmental and psychiatric disorders.
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All data generated or analysed during this study are included in the manuscript.
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (SFB958)
- Craig Curtis Garner
European Research Council (ERC Advanced)
- Christian Rosenmund
Norwegian Cancer Society (early carrier development grant)
- Kay Oliver Schink
National Institute of Health (R01HD053889 and R01HD061575)
- F Kent Hamra
National Center for Research Resources (R24RR03232601)
- F Kent Hamra
The Office of the Director (R24OD011108)
- F Kent Hamra
European Research Council (ERC Advanced,ERC-2011-AdG 294742)
- Zsuzsanna Izsvák
Bundesministerium für Bildung und Forschung (NGFN-2,NGFNplus - ENGINE)
- Zsuzsanna Izsvák
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures for experiments involving animals, were approved by the animal welfare committee of Charité Medical University and the Berlin state government (protocol number: T0036/14, O0208/16).
Reviewing Editor
- Hugo J Bellen, Baylor College of Medicine, United States
Publication history
- Received: March 6, 2019
- Accepted: May 9, 2019
- Accepted Manuscript published: May 10, 2019 (version 1)
- Version of Record published: May 29, 2019 (version 2)
Copyright
© 2019, Ackermann 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.
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Further reading
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Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.
Methods: Using cross-sectional data from 306 previously-concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.
Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.
Conclusions: Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity.
Funding: financial support for this work from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (GIG), an Ontario Graduate Scholarship (SS), a Restracomp Research Fellowship provided by the Hospital for Sick Children (SS), an Institutional Research Chair in Neuroinformatics (MD), as well as a Natural Sciences and Engineering Research Council CREATE grant (MD).
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