Reovirus Serotypes Elicit Distinctive Patterns of Recall Immunity in Humans
Bibliographic record
Abstract
Mammalian orthoreoviruses (reoviruses) are ubiquitous viral agents that infect cells in respiratory and enteric tracts. The frequency and nature of human cellular immunoregulatory responses against reovirus are unknown. Here we establish systems to detect and quantify reovirus-induced cytokine and chemokine recall responses using primary cultures of virus-infected peripheral blood mononuclear cells (PBMC) and two widely used reovirus serotypes, type 1 Lang (T1L) and type 3 Dearing (T3D) reexposure in vitro. In cultures from 44 healthy adults, reovirus induced exceptionally strong CD4 and CD8 T-cell-dependent gamma interferon (IFN-gamma) recall responses concomitant with intense interleukin 10 (IL-10) production. These responses were elicited independently of viral replication. Surprisingly, paired analyses of subject responses to these two common serotypes revealed that while both elicit intense Th1-dominated immunity, median T3D-driven responses were 2.2-fold weaker (P = 0.0004) than those elicited by T1L. Recall responses evoked by these viral serotypes differed markedly in their mechanism of regulation. T3D IL-10 and IFN-gamma responses were CD4 and CD8 dependent and blocked by interfering with CD86 costimulation but were CD80 independent. T1L responses were consistently CD28 and CD80/86 independent. Thus, despite extensive genetic and morphological similarities between reovirus serotypes, the nature and intensity of the human recall responses as well as the control mechanisms regulating them are clearly distinct.
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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) | 0.001 | 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".