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Record W2001294340 · doi:10.1073/pnas.1216011110

Characterization of pathogenic human monoclonal autoantibodies against GM-CSF

2013· article· en· W2001294340 on OpenAlexaff
Yanni Wang, Christy A. Thomson, Lenka L. Allan, Linda M. Jackson, Melanie Olson, Timothy R. Hercus, Tracy L. Nero, Amanda Turner, Michael W. Parker, Angel L. Corbí Lopez, Thomas K. Waddell, Gary P. Anderson, John A. Hamilton, John W. Schrader

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsAutoantibodyImmunologyEpitopePulmonary alveolar proteinosisMonoclonalMonoclonal antibodyAutoimmunityMedicineBiologyAntibodyLungInternal medicine

Abstract

fetched live from OpenAlex

The origin of pathogenic autoantibodies remains unknown. Idiopathic pulmonary alveolar proteinosis is caused by autoantibodies against granulocyte-macrophage colony-stimulating factor (GM-CSF). We generated 19 monoclonal autoantibodies against GM-CSF from six patients with idiopathic pulmonary alveolar proteinosis. The autoantibodies used multiple V genes, excluding preferred V-gene use as an etiology, and targeted at least four nonoverlapping epitopes on GM-CSF, suggesting that GM-CSF is driving the autoantibodies and not a B-cell epitope on a pathogen cross-reacting with GM-CSF. The number of somatic mutations in the autoantibodies suggests that the memory B cells have been helped by T cells and re-entered germinal centers. All autoantibodies neutralized GM-CSF bioactivity, with general correlations to affinity and off-rate. The binding of certain autoantibodies was changed by point mutations in GM-CSF that reduced binding to the GM-CSF receptor. Those monoclonal autoantibodies that potently neutralize GM-CSF may be useful in treating inflammatory disease, such as rheumatoid arthritis and multiple sclerosis, cancer, and pain.

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 score0.330
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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

Citations43
Published2013
Admission routes1
Has abstractyes

Explore more

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