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Record W4390199421 · doi:10.1002/alz.079550

Targeted proteomic search reveals new actors in the synaptic and lysosomal dysfunction in genetic FTD, a GENFI study.

2023· article· en· W4390199421 on OpenAlexaff
Sophia Weiner, Frederika Malichova, Joel Simrén, Mathias Sauer, Imogen J. Swift, Carolin Heller, Kathryn Knowles, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Fermín Moreno, Raquel Sánchez‐Valle, Robert Laforce, Caroline Graff, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Barbara Borroni, Elizabeth Finger, Matthis Synofzik, Daniela Galimberti, Rik Vandenberghe, Alexandre de Mendonça, Christopher Butler, Alexander Gerhard, Simon Ducharme, Isabelle Le Ber, Pietro Tiraboschi, Isabel Santana, Florence Pasquier, Johannes Levin, Markus Otto, Sandro Sorbi, Kaj Blennow, Henrik Zetterberg, Jonathan D. Rohrer, Johan Gobom, Aitana Sogorb‐Esteve

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityUniversity of TorontoWestern UniversitySunnybrook Health Science CentreOccupational Cancer Research CentreUniversité Laval
Fundersnot available
KeywordsC9orf72Frontotemporal dementiaMutationBiologyProteomicsTau proteinPathologicalSynapseBioinformaticsNeuroscienceDementiaGeneticsPsychologyDiseaseMedicineAlzheimer's diseaseInternal medicineGene

Abstract

fetched live from OpenAlex

Abstract Background At present, there are limited fluid biomarkers which measure the underlying pathophysiology of frontotemporal dementia (FTD). Approximately a third of people with FTD have a genetic cause where the pathological basis is well understood. Studying fluid biomarkers in these genetic forms therefore allows greater insight into the relationship between the measure and the underlying disease mechanism. Based on prior work identifying synaptic and lysosomal dysfunction as common mechanisms across the different forms of FTD, we performed a targeted search of proteins related with synapses and the lysosomal pathway on an unbiased proteomic dataset generated from the GENetic FTD Initiative (GENFI) study. Method The dataset was obtained from a total of 248 cerebrospinal fluid samples (CSF) from the GENFI cohort including 109 presymptomatic (44 C9orf72, 39 GRN, 26 MAPT) and 63 symptomatic (34 C9orf72, 17 GRN, 12 MAPT) mutation carriers as well as 76 mutation‐negative controls where Tandem Mass Tag (TMT) proteomics had been performed. We selected the clusters obtained from a Gene Ontology analysis that corresponded to the terms “synapse” and “lysosome” and generated a list of proteins related to each category. We then analysed the differences in each specific protein between genetic groups. Result A total of 42 of the synaptic proteins preselected were significantly changed in symptomatic MAPT mutation carriers, 70 in symptomatic GRN mutation carriers, and 92 in the symptomatic C9orf72 group. Among all of these, there were only 9 proteins that overlapped in the three genetic forms. This list included reelin, which was significantly decreased in each symptomatic group when compared to controls (MAPT p‐value = 0.0016, GRN = 0.0008, C9orf72 = 0.0064), and calretinin, which was significantly increased in symptomatic groups compared to controls (MAPT = 0.0260, GRN = 0.0000, C9orf72 = 0.0100). We then used the same approach to study lysosomal proteins, and found that 38 proteins were significantly changed in symptomatic MAPT, 31 in the symptomatic GRN group and 41 in symptomatic C9orf72 expansion carriers. Conclusion This study provides new insights into candidate markers to assess synaptic and lysosomal dysfunction in FTD.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.318
Teacher spread0.275 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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