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Record W2961559705 · doi:10.3389/fnagi.2019.00195

Exploring the Mitochondrial Degradome by the TAILS Proteomics Approach in a Cellular Model of Parkinson’s Disease

2019· article· en· W2961559705 on OpenAlexaff
Marta Lualdi, Maurizio Ronci, Mara Zilocchi, Federica Corno, Emily Samuela Turilli-Ghisolfi, Mauro Sponchiado, Antonio Aceto, Tiziana Alberio, Mauro Fasano

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsProteomicsProteomeProteasesMitochondrionBiologyCell biologyParkinson's diseaseComputational biologyBiochemistryDiseaseEnzymeMedicinePathologyGene

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is the second most frequent neurodegenerative disease worldwide and the availability of early biomarkers and novel biotargets represents an urgent medical need. The main pathogenetic hallmark of PD is the specific loss of nigral dopaminergic neurons, in which mitochondrial dysfunction plays a crucial role. Mitochondrial proteases are central to the maintenance of healthy mitochondria and they have recently emerged as drug targets. However, an exhaustive characterization of these enzymes and their targets is still lacking, due to difficulties in analyzing proteolytic fragments by bottom-up proteomics approaches. Here, we propose the "mitochondrial dimethylation-TAILS" strategy, which combines the isolation of mitochondria with the enrichment of N-terminal peptides to analyze the mitochondrial N-terminome. We applied this method in a cellular model of altered dopamine homeostasis in neuroblastoma SH-SY5Y cells, which recapitulates early steps of PD pathogenesis. The main aim was to identify candidate mitochondrial proteases aberrantly activated by dopamine dysregulation and their cleaved targets. The proposed degradomics workflow was able to improve the identification of mitochondrial proteins if compared to classical shotgun analysis. In detail, 40% coverage of the mitochondrial proteome was obtained, the sequences of the transit peptides of two mitochondrial proteins were unveiled, and a consensus cleavage sequence for proteases involved in the processing of mitochondrial proteins was depicted. Mass spectrometry proteomics data have been submitted to ProteomeXchange with the identifier PXD013900. Moreover, sixty-one N-terminal peptides whose levels were affected by dopamine treatment were identified. By an in-depth analysis of the proteolytic peptides included in this list, eleven mitochondrial proteins showed altered proteolytic processing. One of these proteins (i.e., the 39S ribosomal protein L49 - MRPL49) was cleaved by the neprilysin protease, already exploited in clinics as a biotarget. We eventually demonstrated a mitochondrial subcellular localization of neprilysin in human cells for the first time. Collectively, these results shed new light on mitochondrial dysfunction linked to dopamine imbalance in PD and opened up the possibility to explore the mitochondrial targets of neprilysin as candidate biomarkers.

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 score0.002

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 integrity0.0000.001
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 head0.043
GPT teacher head0.237
Teacher spread0.194 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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Same venueFrontiers in Aging NeuroscienceSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207