Immunization Elicits Antigen-Specific Antibody Sequestration in Dorsal Root Ganglia Sensory Neurons
Bibliographic record
Abstract
The immune and nervous systems are two major organ systems responsible for host defense and memory. Both systems achieve memory and learning that can be retained, retrieved, and utilized for decades. Here, we report the surprising discovery that peripheral sensory neurons of the dorsal root ganglia (DRG) of immunized mice contain antigen-specific antibodies. Using a combination of rigorous molecular genetic analyses, transgenic mice, and adoptive transfer experiments, we demonstrate that DRGs do not synthesize these antigen-specific antibodies, but rather sequester primarily IgG1 subtype antibodies. As revealed by RNA-seq and targeted qPCR, DRG sensory neurons harvested from either naïve or immunized mice lack enzymes (i.e., RAG1, RAG2, AID, or UNG) required for generating antibody diversity, and therefore, cannot make antibodies. Additionally, transgenic mice that express a reporter fluorescent protein under the control of Ig 1 constant region fail to express Ighg1 transcripts in DRG sensory neurons. Further, neural sequestration of antibodies occurs in mice rendered deficient in neuronal Rag2, but antibody sequestration is not observed in DRG sensory neurons isolated from mice that lack mature B cells (e.g., Rag1 knock out (KO) or µMT mice). Finally, adoptive transfer of Rag1-deficient bone marrow into wild-type mice or wild-type bone marrow into Rag1 KO mice revealed that antibody sequestration was observed in DRG sensory neurons of chimeric mice with wild-type bone marrow but not with Rag1-deficient bone marrow. Together, these results indicate that DRG sensory neurons sequester and retain antigen-specific antibodies released by antibody-secreting plasma cells. Coupling this work with previous studies implicating DRG sensory neurons in regulating antigen trafficking during immunization raises the interesting possibility that the nervous system collaborates with the immune system to regulate antigen-mediated responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".