The role of intestinal microbiota in the humoral response to SARS-CoV-2 after mRNA-1273 vaccination

Abstract

The gut microbiota plays a key role in shaping immune responses, including those induced by vaccination. Its impact on the humoral response to mRNA-based SARS-CoV-2 vaccines, however, remains underexplored. We analyzed gut microbiota composition and anti-Spike (S) IgG levels in 50 healthcare workers vaccinated with the mRNA-1273 SARS-CoV-2 vaccine. Participants were stratified into low, medium, and high responders based on IgG titers 30 days post-vaccination. Stool samples were collected at baseline, and 16 S rRNA sequencing was used to assess microbiota diversity and taxonomic profiles. Alpha diversity indices showed no significant differences across response groups. However, specific microbial signatures were associated with vaccine response. Higher relative abundance of Clostridia, Clostridiales, Ruminococcaceae, and Odoribacter splanchnicus correlated with stronger IgG responses. Functional microbiome analysis revealed enrichment of acetate-producing pathways in high responders (p = 0.012), suggesting a role for short-chain fatty acids in enhancing vaccine-induced immunity. Logistic regression and Random Forest models identified these taxa as predictors of strong antibody responses. The area under the ROC curve (AUC) for individual taxa ranged from 0.70 to 0.76, indicating moderate predictive performance. Conversely, taxa such as Hallella and Sutterella wadsworthensis were linked to lower responses. These findings support a modulatory role of the gut microbiota in mRNA vaccine immunogenicity and highlight microbial metabolic functions as potential targets to boost vaccine efficacy in personalized immunization strategies.

Keywords: 16S rRNA sequencing; Bacterial taxa and immunogenicity; Gut microbiota; MRNA-1273; Microbiome and immunity; SARS-CoV-2 vaccination.

Fig. 1

Relative importance of bacterial taxa in relation to strong antibody responses, assessed by permutation using Random Forest. Relative importance of gut microbial taxa in predicting antibody response groups, as determined by a Random Forest model using permutation-based variable importance. Bars represent the mean decrease in model accuracy when each taxon is permuted, indicating its contribution to the classification performance. AUC: Area under the ROC curve.

Fig. 2

Odds ratio analysis of metabolic functions associated with response level (Low: LR + MR vs. High: HR). Forest plot displaying odds ratios (OR) with 95% confidence intervals (CI) for the presence of microbial metabolic functions comparing low responders (LR and MR) versus high responders (HR). An OR > 1 indicates the function is more frequent in high responders, while an OR < 1 suggests greater presence in low responders. OR = 1 is represented by the vertical dashed line.

Fig. 3

Antibody levels by normal vs. low abundance of acetate-producing and good bacteria. Antibody levels by normal vs. low abundance of acetate-producing and good bacteria. (a) Antibody levels one month after the second dose among individuals with normal vs. low levels of acetate-producing bacteria, p = 0.033. (b) Antibody levels three months after the second dose among individuals with normal vs. low levels of acetate-producing bacteria, p = 0.017. (c) Antibody levels one month after the second dose among individuals with normal vs. low levels of good bacteria, p = 0.027. (d) Antibody levels three months after the second dose among individuals with normal vs. low levels of good bacteria, p = 0.027. BAU/mL: binding antibody units per milliliter; Normal: normal levels of acetate-producing bacteria or good bacteria; Low: low levels of acetate-producing bacteria or good bacteria. For each metabolic function, abundance levels were categorized as “normal” or “low” according to the reference thresholds established by the MicroABT software, version 1.3.0.