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Nitration of Microtubules Blocks Axonal Mitochondrial Transport in a Human Pluripotent Stem Cell Model of Parkinson's Disease

2018· article· en· W4253254180 on OpenAlexafffundabout
Morgan G. Stykel, Mathew P. Kirby, Christopher Czaniecki, Kayla Humphries, Tammy L. Ryan, Scott D. Ryan

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInduced pluripotent stem cellRotenoneMitochondrionBiologyCell biologySubstantia nigraNeurotoxicityDopaminergicMutationParkinson's diseaseNeuroscienceGeneticsInternal medicineDopamineDiseaseMedicineGeneToxicityEmbryonic stem cell

Abstract

fetched live from OpenAlex

Introduction Neuronal loss in Parkinson's disease (PD) is associated with both accumulation of aggregated α‐synuclein (α‐syn) and impaired mitochondrial function in dopaminergic neurons of the substantia nigra. These impairments are closely associated with the accumulation of reactive nitrogen species (RNS) such as nitric oxide and peroxynitrite. Moreover, exposure to mitochondrial toxins, such as the agrochemicals paraquat, maneb and rotenone, are associated with at least a 2.5‐fold increased risk of PD. Further, in patients with a familial mutation in the α‐syn gene ( eg. SNCA ‐A53T), agrochemical exposure correlates with disease onset at an earlier age. While a mechanistic link between α‐syn aggregation and mitochondrial dysfunction has been difficult to ascertain, events seem to center on RNS generation. We thus explore a “two‐hit” hypothesis whereby an α‐syn mutation makes neurons susceptible to mitochondrial dysfunction following agrochemical exposure. Approach Using patient derived induced pluripotent stem cells harbouring the SNCA‐ A53T mutation and genetically corrected ( SNCA ‐Corr) isogenic controls, we tested whether there exists a gene‐by‐environment interaction in PD neurons that results in defective mitochondrial transport. Stem cells were transformed to stably express a mitochondria‐targeted DSRed fluorophore, allowing for live imaging of mitochondrial dynamics. Cells were then differentiated to dopaminergic neurons using a differentiation paradigm that mirrors floor plate development. Subsequently, neurons were exposed to agrochemicals at levels below EPA‐reported lowest observable effect levels (LOEL). Results Although there was no observable difference in the percentage of motile mitochondria between SNCA ‐A53T and SNCA ‐Corr neurons under basal conditions, agrochemical exposure impaired anterograde mitochondrial transport specifically in SNCA ‐A53T neurons. We further demonstrate that agrochemical exposure resulted in increased RNS production in SNCA ‐A53T neurons, leading to nitration of microtubules in SNCA ‐A53T neurons. This modification inhibited KIF5, an important member of the anterograde mitochondrial transport complex, from associating with microtubules. This impairment was rescued by blocking RNS accumulation with the nitric oxide synthase inhibitor, l ‐NAME. Significance Collectively, our results are the first to demonstrate a gene‐by‐environment interaction in PD whereby agrochemical exposure selectively triggers a deficit in mitochondrial transport in PD patient‐derived neurons harboring the SNCA ‐A53T mutation. Support or Funding Information This work was supported in part by the Parkinson Society of Canada (2014‐685 to SDR), the Natural Sciences and Engineering Research Council of Canada (RG060805 to SDR) and an Ontario Graduate Scholarship to MGS. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2018
Admission routes3
Has abstractyes

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