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Record W2067045618 · doi:10.1016/j.jim.2012.08.016

Purification and identification of cell surface antigens using lamprey monoclonal antibodies

2012· article· en· W2067045618 on OpenAlexaff
Cuiling Yu, Shabab Ali, Jonathan St‐Germain, Yanling Liu, Xuecong Yu, David L. Jaye, Michael F. Moran, Max D. Cooper, Götz R. A. Ehrhardt

Bibliographic record

VenueJournal of Immunological Methods · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoCanada Research Chairs
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsLampreyMonoclonal antibodyAntigenAntibodyAvidityBiologyMolecular biologyRecombinant DNAImmunologyBiochemistryGene

Abstract

fetched live from OpenAlex

Variable lymphocyte receptor (VLR) B antibodies of the evolutionary distant sea lamprey are structurally distinct from conventional mammalian antibodies. The different protein architecture and large evolutionary distance of jawless vertebrates suggest that VLR antibodies may represent promising tools for biomarker discovery. Here we report the generation of panels of monoclonal VLR antibodies from lamprey larvae immunized with human T cells and the use of a recombinant monoclonal VLR antibody for antigen purification and mass spectrometric identification. We demonstrate that despite predicted low affinity of individual VLR antigen binding units to the antigen, the high avidity resulting from decameric assembly of secreted VLR antibodies allows for efficient antigen capture and subsequent identification by mass spectometry. We show that VLR antibodies detect their antigens with high specificity and can be used in various standard laboratory application techniques. The lamprey antibodies are novel reagents that can complement conventional monoclonal antibodies in multiple scientific research disciplines.

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)0.002
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 score0.095
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.043
GPT teacher head0.347
Teacher spread0.304 · 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

Citations29
Published2012
Admission routes1
Has abstractyes

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