Improving protective humoral immunity at mucosal sites: the regulation and maintenance of IgA-producing plasma cells in the gut 3400
Bibliographic record
Abstract
Abstract Description In the gut, IgA stands out as the predominant antibody produced by plasma cells (PCs), significantly contributing to proactive protection against various pathogens, including viruses. PCs can be broadly classified into short-lived (SLPCs) and long-lived (LLPCs) categories, with the latter capable of providing humoral immunity for decades, yet remaining poorly characterized. There is a lack of understanding of how the longevity of this humoral immune arm is supported and spatially localized. This project aims to address this gap by employing a plasma cell-specific Cre mouse model to temporally track PCs during homeostasis and murine norovirus (MNV), an enteric viral infection. We performed the multimodal single cell sequencing and immunofluorescence imaging on PCs from a novel lineage tracing mouse and learned about differences in transcriptional program of LLPCs and their supportive environment. We are using this information to further elucidate how intrinsic and extrinsic LLPC characteristics in IgA LLPCs promote their survival in intestinal niches compared to SLPCs, particularly related to BCR signaling, pro-survival, chemotaxis and integrins. These discoveries promise to enhance understanding of molecular signatures essential for PC longevity, potentially leading to the development of longer-lasting IgA responses at mucosal sites. Funding Sources HHMI Gilliam Fellowship; NIH 5R21AI37752 Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".