Evidence map›Paper›PMID 32997293›Full record

ArticleMolecular neurobiology2021

A New Synuclein-Transgenic Mouse Model for Early Parkinson's Reveals Molecular Features of Preclinical Disease.

Diana M Hendrickx, Pierre Garcia, Amer Ashrafi, Alessia Sciortino, Kristopher J Schmit, Heike Kollmus, Nathalie Nicot, Tony Kaoma, Laurent Vallar, Manuel Buttini and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular neurobiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 31% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
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  4. Apoptosis signal-regulating kinase 1 (Frontiers in cellular neuroscience · 2024
    Article
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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

11 authors at 4 institutions in 3 countries.

Diana M Hendrickx *Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg. dmhendrickx@gmail.com.ORCID http://orcid.org/0000-0001-7225-4307
Pierre Garcia *Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
Amer AshrafiLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
Alessia SciortinoLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
Kristopher J SchmitLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
Heike KollmusDepartment of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany.ORCID http://orcid.org/0000-0002-9473-8676
Nathalie NicotQuantitative Biology Unit, Luxembourg Institute of Health, Strassen, Luxembourg.
Tony KaomaDepartment of Oncology, Luxembourg Institute of Health, Strassen, Luxembourg.
Laurent VallarGenomics Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg.
Manuel ButtiniLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg. manuel.buttini@uni.lu.ORCID http://orcid.org/0000-0003-1805-0279
Enrico GlaabLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
University of Luxembourg · LULuxembourg Institute of Health · LUBoston Children's Hospital · USHelmholtz Centre for Infection Research · DE

Funding

Fonds National de la Recherche Luxembourg C12/BM/3976013Fonds National de la Recherche Luxembourg C13/BM/5782168Fonds National de la Recherche Luxembourg I1R-BICPFN- 15NCER
6 · The paper itself

Abstract

Understanding Parkinson's disease (PD), in particular in its earliest phases, is important for diagnosis and treatment. However, human brain samples are collected post-mortem, reflecting mainly end-stage disease. Because brain samples of mouse models can be collected at any stage of the disease process, they are useful in investigating PD progression. Here, we compare ventral midbrain transcriptomics profiles from α-synuclein transgenic mice with a progressive, early PD-like striatal neurodegeneration across different ages using pathway, gene set, and network analysis methods. Our study uncovers statistically significant altered genes across ages and between genotypes with known, suspected, or unknown function in PD pathogenesis and key pathways associated with disease progression. Among those are genotype-dependent alterations associated with synaptic plasticity and neurotransmission, as well as mitochondria-related genes and dysregulation of lipid metabolism. Age-dependent changes were among others observed in neuronal and synaptic activity, calcium homeostasis, and membrane receptor signaling pathways, many of which linked to G-protein coupled receptors. Most importantly, most changes occurred before neurodegeneration was detected in this model, which points to a sequence of gene expression events that may be relevant for disease initiation and progression. It is tempting to speculate that molecular changes similar to those changes observed in our model happen in midbrain dopaminergic neurons before they start to degenerate. In other words, we believe we have uncovered molecular changes that accompany the progression from preclinical to early PD.

Indexed as

Agingalpha-SynucleinAnimalsCorpus StriatumDisease Models, AnimalFemaleGene Expression ProfilingGene Expression Regulation, DevelopmentalGene OntologyGene Regulatory NetworksGenotypeHumansMice, TransgenicNerve DegenerationParkinson DiseaseSubstantia Nigraalpha-SynucleinSnca protein, mouseDisease-stageNetwork analysisParkinson’s diseasePathway analysisTransgenic mouse modelα-Synuclein

Identifiers

PMID32997293
PMCPMC8219584
OpenAlexW3090903109

What OpenQuestion holds

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Registered trials

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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.