PREreview of Early-life gut microbiome is associated with immune response to the oral rotavirus vaccine in healthy infants in the US

Summary: 

This manuscript examines the link between the early-life gut microbiome and the immune response to the RotaTeq oral rotavirus vaccine (ORV) in a cohort of healthy infants in the United States. This is the first study to establish this association in a high-income country setting, minimizing the environmental and pathogen-related confounders prevalent in previous low-middle-income country research. By analyzing longitudinal microbiome data and vaccine response (Rotavirus-IgA titers), the authors demonstrate that the composition and diversity of the gut microbiota at around 1 month of age are associated with vaccine-induced immune responses at 6 months. They identify specific bacterial taxa whose abundance correlates with better or poorer vaccine responses, adding to the understanding of microbiome-vaccine interactions in infants.

Strengths:

The paper's key strengths lie in its use of longitudinal sampling coupled with 16S rRNA sequencing which provides valuable temporal insights into microbiome development and its relation to vaccine response. The authors employ advanced statistical models to adjust for potential confounders and appropriately address the compositional nature of microbiome data. Furthermore, findings could inform strategies to optimize vaccine efficacy in high-income settings, potentially through microbiome-targeted interventions.

Major Revisions: 

The manuscript emphasizes correlational associations between microbiome features and immune responses. However, it should clearly specify the limitations surrounding mechanistic interpretations, given the observational design. Currently, the language may overstate causality; revisions should temper claims accordingly. 

The relatively small cohort, especially after sub-sampling for microbiome analysis, may limit the robustness of some associations. The manuscript should include a candid discussion of statistical power limitations, particularly regarding subgroup analyses, to limit overgeneralization of findings. 

In Table 1, the interval between the final vaccine dose and sample collection varies widely, averaging 45.92 ± 35.75 days at M6 and 201.48 ± 48.08 days at M12. The authors should discuss how this variability may affect immunogenicity measurements and interpretation, as differences in timing could confound antibody response estimates

While the claim that microbiome diversity in high-income settings influences vaccine immunogenicity is impactful, the authors should ensure that their discussion appropriately contextualizes potential demographic and environmental differences that may influence microbiome composition outside of their cohort.

The authors should explicitly state whether race/ethnicity was included as a covariate in the final M1 diversity-IgA titer regression model. Given its strong association with the outcome (Table 5) and its correlation with social determinants of health that could influence the microbiome. This needs to be clarified and, ideally, modeled. The discussion on this point should be strengthened by proposing a broader range of potential factors (e.g., social, neighborhood, or genetic differences beyond FUT2). Furthermore, limiting participation to English-speaking women may introduce dietary bias, narrowing microbiome diversity and reducing generalizability, primarily if the study aims to broadly characterize the U.S. infant microbiome or inform public health recommendations. This should also be addressed in the discussion. 

Minor revisions: 

The authors should clarify which age variable was used in the linear regression model for the core finding: "The relationship between overall microbial community composition at M1 and antibody responses was tested using kernel-based association methods."Given the separation in time, specify if this refers to infant age at M1 (for the microbiome) or infant age at M6 (for the IgA titer), or if a variable accounting for the time gap was used. 

Conclusion: 

Overall, this is a successful study with a generally robust methodology, particularly the use of appropriate statistical methods for compositional data. The manuscript warrants publication, but a few key issues must be addressed to ensure the rigor and validity of the interpretation, particularly concerning potential confounding variables.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they used generative AI to come up with new ideas for their review.