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Record W3194010023 · doi:10.1016/j.celrep.2021.109581

Microglial lysosome dysfunction contributes to white matter pathology and TDP-43 proteinopathy in GRN-associated FTD

2021· article· en· W3194010023 on OpenAlexaff
Yanwei Wu, Wei Shao, Tiffany W. Todd, Jimei Tong, Mei Yue, Shunsuke Koga, Monica Castanedes‐Casey, Ariston L Librero, Chris W. Lee, Ian R. Mackenzie, Dennis W. Dickson, Yong‐Jie Zhang, Leonard Petrucelli, Mercedes Prudencio

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthAlzheimer's AssociationNational Institute of Neurological Disorders and StrokeMayo Clinic
KeywordsMicrogliaMyelinFrontotemporal dementiaWhite matterCathepsin DNeuroinflammationBiologyFrontotemporal lobar degenerationKnockout mousePathologyCell biologyLysosomeNeuroscienceImmunologyMedicineDementiaDiseaseInflammationGeneCentral nervous systemGeneticsEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Loss-of-function mutations in the progranulin gene (GRN), which encodes progranulin (PGRN), are a major cause of frontotemporal dementia (FTD). GRN-associated FTD is characterized by TDP-43 inclusions and neuroinflammation, but how PGRN loss causes disease remains elusive. We show that Grn knockout (KO) mice have increased microgliosis in white matter and an accumulation of myelin debris in microglial lysosomes in the same regions. Accumulation of myelin debris is also observed in white matter of patients with GRN-associated FTD. In addition, our findings also suggest that PGRN insufficiency in microglia leads to impaired lysosomal-mediated clearance of myelin debris. Finally, Grn KO mice that are deficient in cathepsin D (Ctsd), a key lysosomal enzyme, have augmented myelin debris and increased neuronal TDP-43 pathology. Together, our data strongly imply that PGRN loss affects microglial activation and lysosomal function, resulting in the accumulation of myelin debris and contributing to TDP-43 pathology.

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 score0.410
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.244
Teacher spread0.233 · 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

Citations86
Published2021
Admission routes1
Has abstractyes

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