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Record W1567942330 · doi:10.4049/jimmunol.165.6.3358

An α-Helical Cationic Antimicrobial Peptide Selectively Modulates Macrophage Responses to Lipopolysaccharide and Directly Alters Macrophage Gene Expression

2000· article· en· W1567942330 on OpenAlexafffund
Monisha G. Scott, Carrie M. Rosenberger, Michael R. Gold, B. Brett Finlay, Robert E. W. Hancock

Bibliographic record

VenueThe Journal of Immunology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilMedical Research Council Canada
KeywordsGene expressionLipopolysaccharidePeptideGeneCell biologyAntimicrobial peptidesMelittinMacrophageBiologyMolecular biologyChemistryBiochemistryIn vitroImmunology

Abstract

fetched live from OpenAlex

Certain cationic antimicrobial peptides block the binding of LPS to LPS-binding protein and reduce the ability of LPS to induce the production of inflammatory mediators by macrophages. To gain a more complete understanding of how LPS activates macrophages and how cationic peptides influence this process, we have used gene array technology to profile gene expression patterns in macrophages treated with LPS in the presence or the absence of the insect-derived cationic antimicrobial peptide CEMA (cecropin-melittin hybrid). We found that CEMA selectively blocked LPS-induced gene expression in the RAW 264.7 macrophage cell line. The ability of LPS to induce the expression of >40 genes was strongly inhibited by CEMA, while LPS-induced expression of another 16 genes was relatively unaffected. In addition, CEMA itself induced the expression of a distinct set of 35 genes, including genes involved in cell adhesion and apoptosis. Thus, CEMA, a synthetic alpha-helical peptide, selectively modulates the transcriptional response of macrophages to LPS and can alter gene expression in macrophages.

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)0.000
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 score0.010
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.240
Teacher spread0.231 · 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

Citations105
Published2000
Admission routes2
Has abstractyes

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