MétaCan
Menu
Back to cohort
Record W3111465660 · doi:10.1002/alz.045492

Phosphorylated tau interactome in the human Alzheimer’s disease brain

2020· article· en· W3111465660 on OpenAlexaff
Eleanor Drummond, Geoffrey Pires, Claire MacMurray, Manor Askenazi, Shruti Nayak, Marie Bourdon, Jiri Safar, Beatrix Ueberheide, Thomas Wısnıewskı

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsInteractomeProteomicsChemistryTau proteinImmunoprecipitationPhosphorylationProteomeCell biologyAlzheimer's diseaseBiologyBiochemistryDiseasePathologyMedicineGene

Abstract

fetched live from OpenAlex

Abstract Background Accumulation of phosphorylated tau (pTau) is a key pathological feature of Alzheimer’s disease (AD). pTau accumulation causes synaptic impairment, neuronal dysfunction and formation of neurofibrillary tangles (NFTs). The pathological actions of pTau are mediated by surrounding neuronal proteins, however our knowledge of the specific proteins that pTau interacts with in AD is surprisingly limited. Therefore, we used two complementary proteomics approaches to map the pTau interactome for the first time. Method We performed quantitative proteomics on NFTs microdissected from patients with advanced AD. Second, we used affinity purification‐mass spectrometry (AP‐MS) to pinpoint which of the proteins identified directly interacted with pTau. Specific proteins of interest were validated using targeted co‐immunoprecipitation (co‐IP) and immunohistochemistry (IHC). Result 542 proteins were identified in NFTs including the detection of many proteins known to be present in NFTs (e.g. MAPT, UBB and APOE). AP‐MS confirmed that 75 proteins present in NFTs directly interacted with pTau. 54 of these proteins have been previously associated with tau, therefore validating our proteomic approach. More importantly, 15 of these proteins were previously known to be associated with AD, but not directly linked to tau (e.g. VAMP2, HNRNPA1). Network analysis showed that the pTau interactome was enriched in proteins involved in the protein ubiquitination pathway and phagosome maturation, and we were able to pinpoint specific proteins that pTau interacts with in these pathways. Importantly, we also identified six novel proteins, not previously known to be associated with pTau or AD. Among these, Secernin‐1 (SCRN1) was selected for validation. Co‐IP of SCRN1 confirmed its interaction with pTau in AD brains, and IHC showed that SCRN1 was uniquely associated with pTau pathology in AD and not in other neurodegenerative diseases such as Pick’s disease, progressive supranuclear palsy, and corticobasal degeneration. Conclusion Combined, our results provide a roadmap for future studies examining the pathological effects of pTau and for identifying possible new drug targets and biomarkers such as SCRN1 for AD.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.338
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 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicAlzheimer's disease research and treatmentsFrench-language works237,207