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Record W2166895287 · doi:10.1189/jlb.72.6.1234

Regulation of gene expression in mouse macrophages stimulated with bacterial CpG-DNA and lipopolysaccharide

2002· article· en· W2166895287 on OpenAlexfundno aff
Jian Gao, Veronica Diesl, Tatiana Wittmann, David C. Morrison, John L. Ryan, Stefanie N. Vogel, Maximillian T. Follettie

Bibliographic record

VenueJournal of Leukocyte Biology · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
FundersInstitute of GeneticsNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthAmerican Heart Association
KeywordsTLR9BiologyToll-Like Receptor 9CpG siteInnate immune systemMolecular biologyCpG OligodeoxynucleotideGeneCell biologyGene expressionDNA methylationReceptorGenetics

Abstract

fetched live from OpenAlex

CpG-DNA is known as a potent immunostimulating agent and may contribute in therapeutic treatment of many immune disorders. CpG-DNA triggers innate and acquired immune responses through activated expression of various genes in immune cells, including macrophages. To define the molecular mechanism(s) by which CpG-DNA activates immune cells, we studied macrophage gene expression following CpG-DNA exposure using high-density oligonucleotide microarrays. As CpG-DNA receptor Toll-like receptor 9 (TLR9) shares homology with the lipopolysaccharide (LPS)-TLR4 receptor, we compared gene expression profiles in macrophages stimulated by LPS versus CpG-DNA. CpG-DNA and LPS modulate expression of many genes encoding cytokines, cell surface receptors, transcription factors, and proteins related to cell proliferation/differentiation. However, LPS modulated expression of significantly more genes than did CpG-DNA, and all genes induced or repressed by CpG-DNA were induced or repressed by LPS. We conclude that CpG-DNA signaling through TLR9 activates a subset of genes induced by LPS-TLR4 signaling.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.431

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations72
Published2002
Admission routes1
Has abstractyes

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