Mechanisms regulating the turnover of synaptic vesicle (SV) proteins are not well understood. They are thought to require poly-ubiquitination and degradation through proteasome, endo-lysosomal or autophagy-related pathways. Bassoon was shown to negatively regulate presynaptic autophagy in part by scaffolding Atg5. Here, we show that increased autophagy in Bassoon knockout neurons depends on poly-ubiquitination and that the loss of Bassoon leads to elevated levels of ubiquitinated synaptic proteins per se. Our data show that Bassoon knockout neurons have a smaller SV pool size and a higher turnover rate as indicated by a younger pool of SV2. The E3 ligase Parkin is required for increased autophagy in Bassoon-deficient neurons as the knockdown of Parkin normalized autophagy and SV protein levels and rescued impaired SV recycling. These data indicate that Bassoon is a key regulator of SV proteostasis and that Parkin is a key E3 ligase in the autophagy-mediated clearance of SV proteins.
All data generated or analysed during this study are included in the manuscript.
- Craig Curtis Garner
- Eckart D Gundelfinger
- Eckart D Gundelfinger
- Karl-Heinz Smalla
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: Breeding of animals and experiments using animal material were carried out in accordance with the European Communities Council Directive (2010/63/EU) and approved by the local animal care committees of Sachsen-Anhalt or the animal welfare committee of Charité Medical University and the Berlin state government (protocol number: T0036/14, O0208/16).
- Eunjoon Kim, Institute for Basic Science, Korea Advanced Institute of Science and Technology, Republic of Korea
© 2020, Hoffmann-Conaway 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 computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.
Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.