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Record W3011641153 · doi:10.1101/2020.03.17.989699

Vaccine Elicitation of HIV Broadly Neutralizing Antibodies from Engineered B cells

2020· preprint· en· W3011641153 on OpenAlexaff
Deli Huang, Jenny Tuyet Tran, Alex Olson, Thomas Vollbrecht, Mariia V. Guryleva, Mary Tenuta, Roberta Fuller, Torben Schiffner, Justin R. Abadejos, Lauren Couvrette, Tanya R. Blane, Karen Saye, Wenjuan Li, Elise Landais, Alicia González-Martín, William R. Schief, Ben Murrell, Dennis R. Burton, David Nemazee, James E. Voss

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Ottawa
FundersNational Institutes of HealthBill and Melinda Gates Foundation
KeywordsSomatic hypermutationAntibodyBiologyVirologyGenetically engineeredB cellImmunizationViremiaNeutralizing antibodyImmunologyTransduction (biophysics)GeneGenetics

Abstract

fetched live from OpenAlex

HIV broadly neutralizing antibodies (bnAbs) can suppress viremia and protect against infection 1 . However, their elicitation is made difficult by low frequencies of appropriate precursor B cell receptors and the complex maturation pathways required to generate bnAbs from these precursors 2 . Antibody genes can be engineered into B cells for expression as both a functional receptor on cell surfaces and as secreted antibody 3–5 . Here, we show that HIV bnAb-engineered primary mouse B cells can be adoptively transferred and vaccinated in immunocompetent wild-type animals resulting in the expansion of durable bnAb memory and long-lived plasma cells. Somatic hypermutation after immunization indicated that engineered cells have the capacity to respond to an evolving pathogen. These results encourage further exploration of engineered B cell vaccines as a strategy for durable elicitation of HIV bnAbs to protect against infection and as a contributor to a functional cure.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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.

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

Citations24
Published2020
Admission routes1
Has abstractyes

Explore more

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