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Article details
Title
A Sequential Vesicle Pool Model with a Single Release Sensor and a Ca2+-Dependent Priming Catalyst Effectively Explains Ca2+-Dependent Properties of Neurosecretion
Published in
PLoS Computational Biology, December 2013
DOI 10.1371/journal.pcbi.1003362
Pubmed ID
Authors
Abstract

Neurotransmitter release depends on the fusion of secretory vesicles with the plasma membrane and the release of their contents. The final fusion step displays higher-order Ca(2+) dependence, but also upstream steps depend on Ca(2+). After deletion of the Ca(2+) sensor for fast release - synaptotagmin-1 - slower Ca(2+)-dependent release components persist. These findings have provoked working models involving parallel releasable vesicle pools (Parallel Pool Models, PPM) driven by alternative Ca(2+) sensors for release, but no slow release sensor acting on a parallel vesicle pool has been identified. We here propose a Sequential Pool Model (SPM), assuming a novel Ca(2+)-dependent action: a Ca(2+)-dependent catalyst that accelerates both forward and reverse priming reactions. While both models account for fast fusion from the Readily-Releasable Pool (RRP) under control of synaptotagmin-1, the origins of slow release differ. In the SPM the slow release component is attributed to the Ca(2+)-dependent refilling of the RRP from a Non-Releasable upstream Pool (NRP), whereas the PPM attributes slow release to a separate slowly-releasable vesicle pool. Using numerical integration we compared model predictions to data from mouse chromaffin cells. Like the PPM, the SPM explains biphasic release, Ca(2+)-dependence and pool sizes in mouse chromaffin cells. In addition, the SPM accounts for the rapid recovery of the fast component after strong stimulation, where the PPM fails. The SPM also predicts the simultaneous changes in release rate and amplitude seen when mutating the SNARE-complex. Finally, it can account for the loss of fast- and the persistence of slow release in the synaptotagmin-1 knockout by assuming that the RRP is depleted, leading to slow and Ca(2+)-dependent fusion from the NRP. We conclude that the elusive 'alternative Ca(2+) sensor' for slow release might be the upstream priming catalyst, and that a sequential model effectively explains Ca(2+)-dependent properties of secretion without assuming parallel pools or sensors.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Austria 1 2%
Unknown 51 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 6 12%
Professor 4 8%
Other 3 6%
Student > Doctoral Student 3 6%
Other 5 10%
Unknown 18 35%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 15 29%
Neuroscience 9 17%
Biochemistry, Genetics and Molecular Biology 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Mathematics 2 4%
Other 3 6%
Unknown 17 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2013.
All research outputs
#22,493,761
of 27,520,826 outputs
Outputs from PLoS Computational Biology
#8,515
of 9,341 outputs
Outputs of similar age
#256,599
of 329,287 outputs
Outputs of similar age from PLoS Computational Biology
#133
of 143 outputs
Altmetric has tracked 27,520,826 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,341 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,287 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.