Tfh cell regulation and the impact on B cells in viral infection 2194
Bibliographic record
Abstract
Abstract Description Host immunity to pathogens is shaped by crosstalk between innate and adaptive cells. NK cells are innate immune cells that can modulate adaptive responses. Following infection, NK cells migrate into the T zone and suppress T follicular helper (Tfh) cells. This inhibits Tfh cell migration into the B cell follicle and their promotion of antibody responses by germinal center (GC) and age-associated B cells (ABCs). We assessed the cellular and molecular mechanisms underlying NK–mediated suppression of Tfh and B cell responses. We utilize antibody depletion and a novel NK cell knockout model to examine NK-Tfh cell interactions during acute LCMV infection. We show that the absence of NK cells results in the expansion of Tfh cells but not Th1 cells following infection. We found that ICOS, a critical activation receptor for Tfh cell development, is more highly expressed in Tfh cells without NK cells. Transcriptional analysis of Tfh cells demonstrated increased upregulation of genes involved in activation without NK cells. NK cells had a temporal upregulation of ICOS ligand (ICOSL) during early Tfh cell development, indicating the interaction between NK and Tfh cells is mediated by ICOS/ICOSL. Moreover, in the absence of NK cells, the frequency of GC B cells and anti-LCMV antibody titers were similar, but ABCs were expanded. These data indicate that NK cells regulate overactivated Tfh cells in viral infection through ICOS-ICOSL interactions, thus altering the ABC response. Funding Sources Supported by NIH R01 AR073912 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".