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Record W4399528212 · doi:10.3389/fnins.2024.1375299

Proteomics and lipidomic analysis reveal dysregulated pathways associated with loss of sacsin

2024· article· en· W4399528212 on OpenAlexaboutno aff
Daniele Galatolo, Silvia Rocchiccioli, Nicoletta Di Giorgi, Flavio Dal Canto, Giovanni Signore, Federica Morani, Elisa Ceccherini, Stefano Doccini, Filippo M. Santorelli

Bibliographic record

VenueFrontiers in Neuroscience · 2024
Typearticle
Languageen
FieldMedicine
TopicGlycogen Storage Diseases and Myoclonus
Canadian institutionsnot available
Fundersnot available
KeywordsProteomicsAutophagyBiologyCell biologyHomeostasisProteomeAtaxiaBioinformaticsGeneBiochemistryNeuroscienceApoptosis

Abstract

fetched live from OpenAlex

Introduction Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a rare incurable neurodegenerative disease caused by mutations in the SACS gene, which codes for sacsin, a large protein involved in protein homeostasis, mitochondrial function, cytoskeletal dynamics, autophagy, cell adhesion and vesicle trafficking. However, the pathogenic mechanisms underlying sacsin dysfunction are still largely uncharacterized, and so attempts to develop therapies are still in the early stages. Methods To achieve further understanding of how processes are altered by loss of sacsin, we used untargeted proteomics to compare protein profiles in ARSACS fibroblasts versus controls. Results Our analyses confirmed the involvement of known biological pathways and also implicated calcium and lipid homeostasis in ARSACS skin fibroblasts, a finding further verified in SH-SY5Y SACS –/– cells. Validation through mass spectrometry-based analysis and comparative quantification of lipids by LC-MS in fibroblasts revealed increased levels of ceramides coupled with a reduction of diacylglycerols. Discussion In addition to confirming aberrant Ca 2+ homeostasis in ARSACS, this study described abnormal lipid levels associated with loss of sacsin.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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