The Regulation of Nucleic Acid Vaccine Responses by the Microbiome

Abstract

Nucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. Although the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle immunization, the microbiome suppresses Ig and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA lipid nanoparticle vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for continued therapeutic development and deployment of these vaccines.

Graphical abstract

FIGURE 1.

Humoral and cellular responses to OVA immunization in GF and SPF mice. (A) OVA-specific IgG1, IgG2c, and IgG2b titers in serum and OVA-stimulated splenocytes. (B) IFN-γ, (C) IL-4, and (D) IL-10 responses from GF or SPF mice either unimmunized (naive) (n = 3) or immunized with with OVA in PBS (n = 17–19), OVA with AS01 (AS01) (n = 6–8), OVA with Alum (Alum) (n = 7–8), or OVA with AddaVax (Addavax) (n = 3–4). ICS flow cytometry was pregated on live, single, CD3⁺ CD4⁺ cells. Samples falling below the level of cytokine detection by ELISA were set at half the level of cytokine detection, which is the beginning of the y-axis, for visualization. Each data point represents an individual mouse from one experiment. Vaccine-matched GF and SPF groups were compared for statistical significance by Mann–Whitney U test, *p < 0.05, **p < 0.01.

FIGURE 2.

Humoral and cellular responses to DNA-HIV-PT123-prime, DNA-HIV-PT123 + gp120 protein-boost vaccination in GF and SPF mice. (A) Summary of DNA-HIV-PT123 and gp120/MF59 vaccine regimen. (B) Serum gp120-specific IgG titers and (C) proportions of IFN-γ⁺ and TNF-α⁺ CD4⁺ T cells and (D) IFN-γ concentration in supernatant of gp120-stimulated splenocyte cultures from immunized (DNA + gp120 + MF59) (n = 11–12) and unimmunized (naive) (n = 4–5) GF and SPF mice. ICS flow cytometry was pregated on live, single, CD3⁺ CD4⁺ cells. Samples falling below the level of IFN-γ detection (15.6 pg/ml) were set at 7.81 pg/ml for visualization. Each data point represents an individual mouse from two experiments that gave very similar results; cohort 1 (circles), cohort 2 (squares). *p < 0.05 (Welch t test), ****p < 0.0001, ***p < 0.001 (Mann–Whitney U test).

FIGURE 3.

Humoral and cellular responses to partial DNA-HIV-PT123 and gp120 vaccine regimens in GF and SPF mice. (A) Summary of DNA-HIV-PT123, gp120 MF59 vaccine regimens employed. (B) Serum gp120-specific IgG titers and (C) proportions of IFN-γ⁺ and TNF-α⁺ CD4⁺ T cells and (D) IFN-γ concentration in supernatant of gp120-stimulated splenocyte cultures from immunized (DNA + gp120 + MF59) (n = 11–12) and unimmunized (naive) (n = 4–5) GF and SPF mice. ICS flow cytometry was pregated on live, single, CD3⁺ CD4⁺ cells. Samples falling below the level of IFN-γ detection (15.6 pg/ml) were set at 7.81 pg/ml for visualization. Each data point represents an individual mouse from one or two experiments, depending on group. There were no significant differences between vaccine-matched GF and SPF groups (Mann–Whitney U test).

FIGURE 4.

Humoral and cellular responses to mRNA-LNP immunization in GF and SPF mice. (A) Serum Spike-specific IgG (B) Spike peptide pool-stimulated CD4⁺ T cell cytokine responses quantified by ICS, and (C) Spike peptide pool-stimulated CD8⁺ T cell cytokine responses quantified by ICS in unimmunized (PBS Control) and mRNA-Spike-LNP-immunized GF (n = 10) and SPF (n = 12) mice from one experiment. *p < 0.05, **p < 0.01 (Mann–Whitney U test).

FIGURE 5.

Flow cytometry profiling of innate immune response to mRNA-LNP in GF and SPF mice. (A) Representative flow cytometry and graphical summaries of Ly6C^hi monocyte gating and monocyte CD11c versus MHC class II expression in PBS-treated or mRNA-LNP-immunized GF and SPF mice. (B) Gating strategy to identify neutrophils and Ly6C⁺, CD64⁺, and CD64⁻Ly6C⁻ DC/macrophage populations among splenocytes from PBS-treated or mRNA-LNP-immunized GF and SPF mice. (C) Graphical summaries of neutrophil and Ly6C⁺, CD64⁺, and CD64⁻ Ly6C⁻ DC/macrophage proportions in PBS-treated or mRNA-LNP–immunized mice. (D) Median fluorescent intensity (MFI) for CD86 and CD80 expression on the specified populations of immune cells from the spleens of GF or SPF mice 24 h after immunization with mRNA-LNP vaccine or PBS control. Each data point on graphs represents a single mouse (n = 3 per PBS group, n = 4 per mRNA-LNP group from one experiment). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 by one-way ANOVA.

FIGURE 6.

Gene expression profiling of innate immune responses to mRNA-LNP immunization in GF and SPF mice. (A) Principal component analysis of NanoString host response gene mRNA counts from PBS-treated or mRNA-LNP–immunized GF and SPF mice. GF and SPF mRNA-LNP–immunized groups separate along principal component 2 (PC2). (B) Pathway scores for myeloid and lymphocyte pathways in mRNA-LNP–immunized GF and SPF mice. Each data point represents an individual mouse (n = 4 per group). (C) Heatmap representation of innate signaling pathway scores in mRNA-LNP–immunized GF and SPF mice. NLR, NOD-like receptor.