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Record W2587120205 · doi:10.1017/s0033583517000026

Unraveling amyloid formation paths of Parkinson's disease protein α-synuclein triggered by anionic vesicles

2017· article· en· W2587120205 on OpenAlexfundno aff
Jūris Kiškis, István Horváth, Pernilla Wittung‐Stafshede, Sandra Rocha

Bibliographic record

VenueQuarterly Reviews of Biophysics · 2017
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersVetenskapsrådetKnut och Alice Wallenbergs StiftelseFondation ChalmersEuropean Commission
KeywordsVesicleChemistryBiophysicsAmyloid (mycology)Circular dichroismAlpha-synucleinVesicle fusionSynaptic vesicleBiochemistryParkinson's diseaseBiologyInternal medicineMembrane

Abstract

fetched live from OpenAlex

Amyloid formation of the synaptic brain protein α-synuclein (αS) is related to degeneration of dopaminergic neurons in Parkinson's disease patients. αS is thought to function in vesicle transport and fusion and it binds strongly to negatively charged vesicles in vitro. Here we combined circular dichroism, fluorescence and imaging methods in vitro to characterize the interaction of αS with negatively charged vesicles of DOPS (1,2-dioleoyl-sn-glycero-3-phospho-L-serine, sodium salt) and DOPG (1,2-dioleoyl-sn-glycero-3-phospho-(1'-rac-glycerol), sodium salt) and the consequences of such interactions on αS amyloid formation. We found that lipid head-group chemistry modulates αS interactions and also affects amyloid fiber formation. During the course of the experiments, we made the unexpected discovery that pre-formed αS oligomers, typically present in a small amount in the αS starting material, acted as templates for linear growth of anomalous amyloid fibers in the presence of vesicles. At the same time, the remaining αS monomers were restricted from vesicle-mediated nucleation of amyloid fibers. Although not a dominant process in bulk experiments, this hidden αS aggregation pathway may be of importance in vivo.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.277
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2017
Admission routes1
Has abstractyes

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