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G2019S-LRRK2 Expression Augments  -Synuclein Sequestration into Inclusions in Neurons

2016· article· en· W2472421578 on OpenAlexaff
Laura A. Volpicelli‐Daley, Hisham Abdelmotilib, Zhiyong Liu, Lindsay E. Stoyka, João Paulo Lima Daher, Austen J. Milnerwood, V. K. Unni, Warren D. Hirst, Zhenyu Yue, Hongfan Zhao, Kyle Fraser, Richard Kennedy, Andrew B. West

Bibliographic record

VenueJournal of Neuroscience · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Neurological Disorders and Stroke
KeywordsLRRK2BiologySubstantia nigraGene knockdownParkinson's diseaseKinaseCell biologyInclusion bodiesAlpha-synucleinProtein kinase domainMutationMutantDopaminergicMolecular biologyPathologyNeuroscienceGeneticsDiseaseGeneDopamineMedicine

Abstract

fetched live from OpenAlex

UNLABELLED: Pathologic inclusions define α-synucleinopathies that include Parkinson's disease (PD). The most common genetic cause of PD is the G2019S LRRK2 mutation that upregulates LRRK2 kinase activity. However, the interaction between α-synuclein, LRRK2, and the formation of α-synuclein inclusions remains unclear. Here, we show that G2019S-LRRK2 expression, in both cultured neurons and dopaminergic neurons in the rat substantia nigra pars compact, increases the recruitment of endogenous α-synuclein into inclusions in response to α-synuclein fibril exposure. This results from the expression of mutant G2019S-LRRK2, as overexpression of WT-LRRK2 not only does not increase formation of inclusions but reduces their abundance. In addition, treatment of primary mouse neurons with LRRK2 kinase inhibitors, PF-06447475 and MLi-2, blocks G2019S-LRRK2 effects, suggesting that the G2019S-LRRK2 potentiation of inclusion formation depends on its kinase activity. Overexpression of G2019S-LRRK2 slightly increases, whereas WT-LRRK2 decreases, total levels of α-synuclein. Knockdown of total α-synuclein with potent antisense oligonucleotides substantially reduces inclusion formation in G2019S-LRRK2-expressing neurons, suggesting that LRRK2 influences α-synuclein inclusion formation by altering α-synuclein levels. These findings support the hypothesis that G2019S-LRRK2 may increase the progression of pathological α-synuclein inclusions after the initial formation of α-synuclein pathology by increasing a pool of α-synuclein that is more susceptible to forming inclusions. SIGNIFICANCE STATEMENT: α-Synuclein inclusions are found in the brains of patients with many different neurodegenerative diseases. Point mutation, duplication, or triplication of the α-synuclein gene can all cause Parkinson's disease (PD). The G2019S mutation in LRRK2 is the most common known genetic cause of PD. The interaction between G2019S-LRRK2 and α-synuclein may uncover new mechanisms and targets for neuroprotection. Here, we show that expression of G2019S-LRRK2 increases α-synuclein mobility and enhances aggregation of α-synuclein in primary cultured neurons and in dopaminergic neurons of the substantia nigra pars compacta, a susceptible brain region in PD. Potent LRRK2 kinase inhibitors, which are being developed for clinical use, block the increased α-synuclein aggregation in G2019S-LRRK2-expressing neurons. These results demonstrate that α-synuclein inclusion formation in neurons can be blocked and that novel therapeutic compounds targeting this process by inhibiting LRRK2 kinase activity may slow progression of PD-associated pathology.

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 score0.834
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.031
GPT teacher head0.313
Teacher spread0.283 · 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

Citations191
Published2016
Admission routes1
Has abstractyes

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