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Record W3119609930 · doi:10.1038/s41398-020-01175-9

A cortical immune network map identifies distinct microglial transcriptional programs associated with β-amyloid and Tau pathologies

2021· article· en· W3119609930 on OpenAlexafffund
Ellis Patrick, Marta Olah, Mariko Taga, Hans‐Ulrich Klein, Jishu Xu, Charles C. White, Daniel Felsky, Sonal Agrawal, Chris Gaiteri, Lori B. Chibnik, Sara Mostafavi, Julie A. Schneider, David A. Bennett, Elizabeth M. Bradshaw, Philip L. De Jager

Bibliographic record

VenueTranslational Psychiatry · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Neurological Disorders and StrokeNatural Sciences and Engineering Research Council of CanadaNational Institute on AgingNational Institutes of HealthAustralian Government
KeywordsNeuroscienceMicrogliaAmyloid βAmyloid (mycology)Schizophrenia (object-oriented programming)Immune systemMedicineBiologyPsychologyDiseaseImmunologyPathologyPsychiatryInflammation

Abstract

fetched live from OpenAlex

Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimer's disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 540 subjects from two cohort studies of brain aging. Two of these transcriptional programs-modules 113 and 114-relate to the accumulation of β-amyloid, while module 5 relates to tau pathology. We replicate these associations in brain epigenomic data and in two independent datasets. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that three representative module 5 genes (ACADVL, TRABD, and VASP) encode proteins that are upregulated in activated microglia in 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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.853

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 head0.028
GPT teacher head0.246
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 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

Citations30
Published2021
Admission routes2
Has abstractyes

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