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Progranulin Deficiency Decreases Gross Neural Connectivity But Enhances Transmission at Individual Synapses

2011· article· en· W1977618591 on OpenAlexafffund
Lucı́a Tapia, Austen J. Milnerwood, Aobo Guo, Fergil Mills, Eileen Yoshida, Cristina Vasuta, Ian R. Mackenzie, Lynn A. Raymond, Max S. Cynader, William Jia, Shernaz X. Bamji

Bibliographic record

VenueJournal of Neuroscience · 2011
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsHaploinsufficiencySynapseGene knockdownNeuroscienceHippocampal formationBiologyHippocampusNeurotransmissionPhenotypeSynaptic vesicleCell biologyVesicleGeneGenetics

Abstract

fetched live from OpenAlex

Frontotemporal dementia (FTD) has been linked to mutations in the progranulin gene (GRN) that lead to progranulin (PGRN) haploinsufficiency. Thus far, our understanding of the effects of PGRN depletion in the brain has been derived from investigation of gross pathology, and more detailed analyses of cellular function have been lacking. We report that knocking down PGRN levels in rat primary hippocampal cultures reduces neural connectivity by decreasing neuronal arborization and length as well as synapse density. Despite this, the number of synaptic vesicles per synapse and the frequency of mEPSCs are increased in PGRN knockdown cells, suggesting an increase in the probability of release at remaining synapses. Interestingly, we demonstrate that the number of vesicles per synapse is also increased in postmortem brain sections from FTD patients with PGRN haploinsufficiency, relative to controls. Our observations show that PGRN knockdown severely alters neuronal connectivity in vitro and that the synaptic vesicle phenotype observed in culture is consistent with that observed in the hippocampus of FTD patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.096
GPT teacher head0.326
Teacher spread0.230 · 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 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

Citations85
Published2011
Admission routes2
Has abstractyes

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