Protective Role of Passively Transferred Maternal Cytokines against Bordetella pertussis Infection in Newborn Piglets
Bibliographic record
Abstract
ABSTRACT Maternal vaccination represents a potential strategy to protect both the mother and the offspring against life-threatening infections. This protective role has mainly been associated with antibodies, but the role of cell-mediated immunity, in particular passively transferred cytokines, is not well understood. Here, using a pertussis model, we have demonstrated that immunization of pregnant sows with heat-inactivated bacteria leads to induction of a wide range of cytokines (e.g., tumor necrosis factor alpha [TNF-α], gamma interferon [IFN-γ], interleukin-6 [IL-6], IL-8, and IL-12/IL-23p40) in addition to pertussis-specific antibodies. These cytokines can be detected in the sera and colostrum/milk of vaccinated sows and subsequently were detected at significant levels in the serum and bronchoalveolar lavage fluid of piglets born to vaccinated sows together with pertussis-specific antibodies. In contrast, active vaccination of newborn piglets with heat-inactivated bacteria induced high levels of specific IgG and IgA but no cytokines. Although the levels of antibodies in vaccinated piglets were comparable to those of passively transferred antibodies, no protection against Bordetella pertussis infection was observed. Thus, our results demonstrate that a combination of passively transferred cytokines and antibodies is crucial for disease protection. The presence of passively transferred cytokines/antibodies influences the cytokine secretion ability of splenocytes in the neonate, which provides novel evidence that maternal immunization can influence the newborn's cytokine milieu and may impact immune cell differentiation (e.g., Th1/Th2 phenotype). Therefore, these maternally derived cytokines may play an essential role both as mediators of early defense against infections and possibly as modulators of the immune repertoire of the offspring.
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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".