Evidence map›Paper›PMID 40517155›Full record

ArticleCommunications chemistry2025

A mechanistic model of pure and lipidic α-synuclein aggregation for advancing Parkinson's therapies.

Elena Righetti, Luca Marchetti, Enrico Domenici, Federico Reali

Abstract read
In one paragraph

Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Elena RighettiFondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. righetti@cosbi.eu.ORCID http://orcid.org/0000-0003-2092-4238
Luca MarchettiFondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.ORCID http://orcid.org/0000-0001-9043-7705
Enrico DomeniciFondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.ORCID http://orcid.org/0000-0001-7436-6919
Federico RealiFondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. reali@cosbi.eu.ORCID http://orcid.org/0000-0002-7891-5695

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alpha-synuclein (aSyn) plays a crucial role in Parkinson's disease, with various aggregates proposed as pathogenic triggers and therapeutic targets. However, anti-aSyn aggregation compounds often fail due to limited knowledge of the underlying molecular basis. In particular, interactions with lipid membranes are central to both physiological and pathological roles of aSyn, yet their underlying mechanisms remain unclear. Disrupting this balance may drive Parkinson's onset and progression, underscoring the need for a mechanistic understanding of pure and lipid-mediated aggregation. Building on well-established in vitro aggregation studies, we propose a mathematical model of aSyn accumulation incorporating both aggregation routes via a nucleation-conversion-polymerization process with self-amplifying loops and toxic oligomers. Model calibration uses data from in vitro assays mimicking physiologically relevant conditions, providing insights into transient and stable aSyn intermediates. Incorporating aSyn-lipid interactions enables in silico exploration of how lipid-to-aSyn ratio influences aggregation, with possible implications for neurodegeneration. Sensitivity analysis highlights secondary nucleation inhibition as a potential anti-aggregation strategy. Overall, our work contributes to a unified framework for investigating in vitro aSyn aggregation and evaluating Parkinson's therapies by building on existing models. It can serve as a stand-alone tool and a modular component in multiscale models, with potential applications in quantitative systems pharmacology.

Identifiers

PMID40517155
PMCPMC12167383

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.