A translational transcriptomic signature of vaccine reactogenicity for the evaluation of novel formulations
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Curated by eLife
eLife Assessment
This study proposes a cross-species transcriptomic framework to predict vaccine reactogenicity, with implications for preclinical vaccine safety assessment. The findings show that mouse muscle transcriptomic signatures capture conserved inflammatory programs and can identify highly reactogenic formulations, with supportive but limited evidence for finer discrimination among licensed human vaccines. Overall, the work establishes a valuable foundation for translational biomarkers of reactogenicity, although the strength of evidence for broad cross-species predictive performance remains incomplete and would benefit from further validation.
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Abstract
Accurately predicting vaccine reactogenicity at the preclinical stage remains a major challenge in vaccine development, as conventional animal studies and in vitro assays capture general inflammation but fail to quantify local or systemic reactogenicity relevant to humans. Using transcriptomic data from the BioVacSafe consortium encompassing seven vaccines and immunostimulants in mice and five licensed vaccines in humans, we developed a cross-compartment and cross-species predictive model of vaccine reactogenicity. Reactogenicity classes were defined in mouse muscle based on the magnitude of transcriptomic responses and literature evidence. A penalized ordinal regression model was trained to predict both discrete classes and continuous scores of reactogenicity. Transcriptomic profiles from mouse muscle were highly predictive of reactogenicity, with key genes enriched in inflammatory and tissue repair pathways such as IL6/JAK/STAT3 signalling. The model retained strong performance when transferred to mouse blood and revealed shared transcriptional programs between compartments, suggesting coordinated innate responses. When applied to human blood, the classifier correctly ranked licensed vaccines by reactogenicity, identifying Fluad (MF59-adjuvanted) as the most reactogenic, in agreement with elevated C-reactive protein and ReactoScore values, while Engerix-B, Varilrix, and Stamaril were classified as low-reactogenicity formulations. These results align with clinical safety data and demonstrate that early transcriptomic signatures in mice can predict human reactogenicity profiles. Our study presents a pan-vaccine, cross-species transcriptomic signature that bridges preclinical and clinical data, offering a foundation for translational biomarkers and mechanism-informed assessment of vaccine tolerability.
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eLife Assessment
This study proposes a cross-species transcriptomic framework to predict vaccine reactogenicity, with implications for preclinical vaccine safety assessment. The findings show that mouse muscle transcriptomic signatures capture conserved inflammatory programs and can identify highly reactogenic formulations, with supportive but limited evidence for finer discrimination among licensed human vaccines. Overall, the work establishes a valuable foundation for translational biomarkers of reactogenicity, although the strength of evidence for broad cross-species predictive performance remains incomplete and would benefit from further validation.
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Reviewer #1 (Public review):
Summary:
The authors aimed to develop a translational framework for predicting vaccine reactogenicity by training a penalized ordinal regression model on mouse muscle transcriptomics and applying it across tissues and species to rank human vaccines by their inflammatory potential.
Strengths:
The study addresses an important gap in preclinical vaccine safety assessment. The identification of IL6/JAK/STAT3 signaling as a key pathway implicated in reactogenicity is biologically plausible, and the observation of coordinated changes between muscle and blood compartments supports the biological relevance of the signature. The model achieves near-perfect classification in mouse muscle tissue and successfully identifies Fluad (MF59-adjuvanted) as the most reactogenic among licensed human vaccines, consistent with …
Reviewer #1 (Public review):
Summary:
The authors aimed to develop a translational framework for predicting vaccine reactogenicity by training a penalized ordinal regression model on mouse muscle transcriptomics and applying it across tissues and species to rank human vaccines by their inflammatory potential.
Strengths:
The study addresses an important gap in preclinical vaccine safety assessment. The identification of IL6/JAK/STAT3 signaling as a key pathway implicated in reactogenicity is biologically plausible, and the observation of coordinated changes between muscle and blood compartments supports the biological relevance of the signature. The model achieves near-perfect classification in mouse muscle tissue and successfully identifies Fluad (MF59-adjuvanted) as the most reactogenic among licensed human vaccines, consistent with clinical safety data.
Weaknesses:
The methodological foundation has several concerns. The reactogenicity class definitions rely on PC1 scores with modest variance explained, yet no sensitivity analyses demonstrate robustness to different normalization strategies, feature selection approaches, or dimensionality reduction methods. I suggest performing sensitivity analyses demonstrating that reactogenicity class definitions are robust to alternative normalization methods, feature selection criteria, and dimensionality reduction approaches.
The combined mouse analysis reveals that tissue effects dominate over vaccine-induced variation, and no explicit batch or compartment correction was reported. The authors can apply batch/compartment correction (e.g., SVA) when analyzing combined mouse muscle and blood data, then recompute PCA and downstream analyses.
The central claim regarding cross-species ranking capability is not fully supported. In human blood, the model largely distinguishes Fluad from other vaccines but shows limited separation among non-Fluad formulations, with many pairwise comparisons yielding non-significant adjusted p-values. This pattern suggests the model may be tuned to detect large inflammatory magnitudes-likely a consequence of training on extreme stimuli such as LPS and whole-cell pertussis-rather than capturing the finer gradations relevant for distinguishing licensed vaccines with moderate reactogenicity profiles. I highly suggest retraining the model, excluding extreme stimuli (LPS, Pentavac), to evaluate whether mid-range separations among licensed vaccines can be recovered.
Impact:
While the conceptual framework is promising, the current evidence does not convincingly demonstrate that the model can rank vaccines beyond identifying highly inflammatory outliers. The utility for preclinical assessment of novel vaccine candidates with moderate reactogenicity profiles remains uncertain.
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Reviewer #2 (Public review):
Summary:
The authors derived a time-specific signature of reactogenicity from mouse muscle following exposure to vaccines /TLRs for capturing the reactogenicity patterns. They tested this reactogenicity signature in mouse blood, and then they applied the reactogenicity signature to human blood from subjects having received different vaccines. They identified biomarkers in mouse muscle which are also observed in mouse and human blood and could be used as a reactogenicity signature in mice, instead of CRP.
Strengths:
(1) The authors used transcriptomic response following vaccination and used common genes to human and mice for defining a reactogenic signature.
(2) As the authors used different formulations in mice, the model was trained across a broad reactogenicity spectrum, which has the advantage of being …
Reviewer #2 (Public review):
Summary:
The authors derived a time-specific signature of reactogenicity from mouse muscle following exposure to vaccines /TLRs for capturing the reactogenicity patterns. They tested this reactogenicity signature in mouse blood, and then they applied the reactogenicity signature to human blood from subjects having received different vaccines. They identified biomarkers in mouse muscle which are also observed in mouse and human blood and could be used as a reactogenicity signature in mice, instead of CRP.
Strengths:
(1) The authors used transcriptomic response following vaccination and used common genes to human and mice for defining a reactogenic signature.
(2) As the authors used different formulations in mice, the model was trained across a broad reactogenicity spectrum, which has the advantage of being used for evaluating new vaccines/vaccine platforms.
Weaknesses:
(1) The muscle gene signature reflects local reactogenicity. Systemic reactogenicity is not specifically addressed, except where overlapping gene signatures are observed for both local and systemic reactogenicity.
(2) In the same logic, could we find additional genes in the blood which are not captured in the muscle?
(3) The peak of the reactogenicity is usually 24h; it is not certain that additional TPs have helped the findings. If they have, the authors should explain.
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