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Record W2989867168 · doi:10.1101/860395

Antibody responses to immunization require sensory neurons

2019· preprint· en· W2989867168 on OpenAlexaff
Aisling Tynan, Téa Tsaava, Manojkumar Gunasekaran, Isabel Snee, Tak W. Mak, Peder S. Olofsson, Jan Andersson, Sangeeta S. Chavan, Kevin J. Tracey

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicIon Channels and Receptors
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical Sciences
KeywordsNociceptorTRPV1AntigenTransient receptor potential channelNeuroscienceImmunizationImmunologyAntibodySensory systemOptogeneticsBiologyMedicineNociceptionReceptorInternal medicine

Abstract

fetched live from OpenAlex

Abstract Mammals store memories in the nervous and immune systems. Sensory neurons have been implicated in enhancing neurological memory, but whether neurons participate during immunity to novel antigens is unknown. Here, mice rendered deficient in transient receptor potential vanilloid 1 (TRPV1)-expressing sensory neurons, termed “nociceptors,” fail to develop competent antibody responses to KLH and hapten-NP. Moreover, selective optogenetic stimulation of TRPV1 neurons during immunization significantly enhanced antibody responses to antigens. Thus, TRPV1 nociceptors mediate antibody responses to novel antigen, and stimulating TRPV1 nociceptors enhances antibody responses during immunization. This is the first genetic and selective functional evidence that nociceptors are required during immunization to produce antigen-specific antibodies. Summary The first genetic and selective functional evidence showing that TRPV1-expressing nociceptors are required for competent antibody responses to novel antigen, and stimulating TRPV1 nociceptors enhances antibody responses to novel antigen.

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)0.002
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 score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.032
GPT teacher head0.273
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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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