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Record W2075666371 · doi:10.3791/2784

Quantitative Analyses of all Influenza Type A Viral Hemagglutinins and Neuraminidases using Universal Antibodies in Simple Slot Blot Assays

2011· article· en· W2075666371 on OpenAlexaff
Caroline Gravel, Changgui Li, Junzhi Wang, Anwar M. Hashem, Bozena Jaentschke, Gary Van Domselaar, Runtao He, Xuguang Li

Bibliographic record

VenueJournal of Visualized Experiments · 2011
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health Agency of CanadaUniversity of OttawaHealth Canada
FundersKing Abdulaziz UniversitySaudi Arabian Cultural Bureau
KeywordsEpitopeVirologyNeuraminidaseHemagglutinin (influenza)Influenza vaccineBiologyAntibodyInfluenza A virusInfluenza A virus subtype H5N1Peptide sequenceAntigenAntiserumImmunogenicityVirusImmunologyBiochemistryGene

Abstract

fetched live from OpenAlex

Hemagglutinin (HA) and neuraminidase (NA) are two surface proteins of influenza viruses which are known to play important roles in the viral life cycle and the induction of protective immune responses(1,2). As the main target for neutralizing antibodies, HA is currently used as the influenza vaccine potency marker and is measured by single radial immunodiffusion (SRID)(3). However, the dependence of SRID on the availability of the corresponding subtype-specific antisera causes a minimum of 2-3 months delay for the release of every new vaccine. Moreover, despite evidence that NA also induces protective immunity(4), the amount of NA in influenza vaccines is not yet standardized due to a lack of appropriate reagents or analytical method(5). Thus, simple alternative methods capable of quantifying HA and NA antigens are desirable for rapid release and better quality control of influenza vaccines. Universally conserved regions in all available influenza A HA and NA sequences were identified by bioinformatics analyses(6-7). One sequence (designated as Uni-1) was identified in the only universally conserved epitope of HA, the fusion peptide(6), while two conserved sequences were identified in neuraminidases, one close to the enzymatic active site (designated as HCA-2) and the other close to the N-terminus (designated as HCA-3)(7). Peptides with these amino acid sequences were synthesized and used to immunize rabbits for the production of antibodies. The antibody against the Uni-1 epitope of HA was able to bind to 13 subtypes of influenza A HA (H1-H13) while the antibodies against the HCA-2 and HCA-3 regions of NA were capable of binding all 9 NA subtypes. All antibodies showed remarkable specificity against the viral sequences as evidenced by the observation that no cross-reactivity to allantoic proteins was detected. These universal antibodies were then used to develop slot blot assays to quantify HA and NA in influenza A vaccines without the need for specific antisera(7,8). Vaccine samples were applied onto a PVDF membrane using a slot blot apparatus along with reference standards diluted to various concentrations. For the detection of HA, samples and standard were first diluted in Tris-buffered saline (TBS) containing 4M urea while for the measurement of NA they were diluted in TBS containing 0.01% Zwittergent as these conditions significantly improved the detection sensitivity. Following the detection of the HA and NA antigens by immunoblotting with their respective universal antibodies, signal intensities were quantified by densitometry. Amounts of HA and NA in the vaccines were then calculated using a standard curve established with the signal intensities of the various concentrations of the references used. Given that these antibodies bind to universal epitopes in HA or NA, interested investigators could use them as research tools in immunoassays other than the slot blot only.

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)0.001
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 score0.016
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.449
GPT teacher head0.589
Teacher spread0.140 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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