Mechanisms and predictors of adjuvanticity and antibody persistence

Abstract

Provided herein are methods for vaccine development, characterization, and validation. Methods are provided for vaccine optimization, selection, and benchmarking, including vaccine adjuvants, using the response signatures disclosed herein. The methods include predicting the longevity of response, e.g., antibody response, of candidate vaccines or vaccine adjuvants, and evaluating similarity to a benchmark reference vaccine.

Description

[Background technology]

[0001] With over 80 million cases and 1.7 million deaths worldwide as of December 2020, COVID-19 represents a serious threat to public health that a global pandemic poses. The ability to rapidly develop vaccines that induce protective immunity against novel pathogens is critical to controlling and preventing such pandemics. As vaccine components that improve the magnitude, breadth, and durability of the immune response, adjuvants are powerful tools for modern vaccine development. By enabling antigen preservation, adjuvants also enable more rapid vaccine production, a key factor during responses to pandemics.

[0002] For over 70 years, insoluble aluminum salts (alum) were the only licensed adjuvants, but in the past 30 years, there has been a significant increase in adjuvants available for licensed vaccines. These include oil-in-water emulsion-based adjuvants (MF59, AS03), the TLR4 agonist 3-O-desacyl-4'-monophosphoryl lipid A (MPL) (AS01, AS04), and adjuvants containing the TLR9 agonist CpG oligonucleotide, CpG1018. Many of these adjuvants, including AS03, MF59, and CpG1018, have been made available by their owners for use in COVID-19 vaccines, and at least 10 developers have indicated plans to create adjuvanted COVID-19 vaccines. Despite this significant growth in adjuvant technology, in many cases the molecular mechanisms by which these adjuvants enhance the immune response to vaccination remain unclear.

[0003] AS03 is a squalene-based oil-in-water emulsion containing α-tocopherol (vitamin E) that has been shown to increase the breadth and magnitude of CD4+ T cell and antibody responses to multiple influenza strains, even compared to MF59. Recent studies in mice have demonstrated that AS03 induces changes in the expression of lipid metabolism-related genes in draining lymph nodes and increased endoplasmic reticulum (ER) stress in macrophages, which drives increased cytokine production and improved antibody responses.

[0004] Additionally, a similar oil-in-water emulsion-based adjuvant, MF59, has been shown to induce local release of extracellular ATP and to be functionally dependent on MyD88 in an inflammasome-independent manner, suggesting that these types of adjuvants result in some degree of cellular damage or stress that leads to the release of damage-associated molecular patterns (DAMPs) and the induction of innate immune responses. However, the molecular pathways by which AS03 promotes these responses remain poorly defined.

[0005] In addition to boosting the initial immune response to vaccination, adjuvants such as AS03 can also improve the longevity of the resulting immunity. Some vaccines, particularly live virus vaccines such as smallpox or yellow fever, can induce lifelong antibody responses, while others, such as those against pertussis and influenza, only promote a transient response and immunity that declines over time, resulting in loss of protection and the need for booster vaccines. With regard to humoral immunity, long-lived plasma cells have been identified as the primary mediator of sustained antibody responses, but the mechanisms required to drive robust long-lived plasma cell differentiation and sustained antibody responses to vaccination are not well understood.

[0006] Live attenuated vaccines, such as smallpox or yellow fever vaccines, induce sustained antibody (Ab) responses that can last a lifetime, but immunity decline has been reported for some vaccines, including mRNA vaccines against COVID-19, as well as subunit vaccines against influenza, malaria, Bordetella pertussis, Salmonella enterica serovar Typhi, and Neisseria meningitidis, and other pathogens. Why some vaccines provide lifelong protection and others only for a few months remains one of the great mysteries of immunology. Currently, the duration of immune protection of new vaccines is difficult to predict during vaccine product development and can only be confirmed by a "wait and see" approach. Thus, a major challenge in vaccinology is to be able to predict how long a vaccine will protect by defining early signatures in the blood (gene signatures or cell-based signatures) that are induced within days of vaccination and predict the durability of the immune response and protection. Summary of the Invention

[0007] Methods are provided for vaccine development and validation. Methods are provided for vaccine optimization, including vaccine adjuvants, and prediction of antibody response durability using the gene signatures disclosed herein. The methods include prediction of response durability, e.g., antibody response longevity, of candidate vaccines or vaccine adjuvants. Vaccines of interest include, for example, live virus vaccines, subunit vaccines, mRNA vaccines, viral vector vaccines, and the like.

[0008] The methods provide a means to predict the durability of response over a short period of time after immunization, for example, less than about 14 days, less than about 10 days, for example, up to or including 7 days. This information provides the great advantage of allowing rapid benchmarking and stratification of vaccine efficacy, shortening the time required for evaluation.

[0009] In one embodiment, a method for predicting the durability of an immune response to a candidate vaccine is provided, the method comprising administering a candidate vaccine, which may include an adjuvant, to a mammal, determining an early gene signature from immune cells, e.g., peripheral blood mononuclear cells (PBMCs), and predicting the durability of the response from the early gene signature. In some embodiments, the immune cells comprise platelets. In some embodiments, the early gene signature is determined by mRNA content from the platelets. In some embodiments, the early gene signature is determined about 7 to about 10 days after immunization. In some embodiments, the mammal is a mouse. In some embodiments, the mammal is a non-human primate. In some embodiments, the mammal is a human. In some embodiments, the analysis of plasma metabolomics is performed on the mammal. In some embodiments, a candidate vaccine or adjuvant is selected for development based on its ability to provide an early gene signature indicative of longer antibody longevity.

[0010] In one embodiment, a method for predicting the durability of an immune response to a candidate vaccine is provided, the method comprising administering a candidate vaccine, which may include an adjuvant, to a mammal and determining platelet RNA content in the recipient following vaccination. In some embodiments, the platelet RNA content is determined about 7 to about 10 days after immunization. In some embodiments, the mammal is a mouse. In some embodiments, the mammal is a non-human primate. In some embodiments, the mammal is a human. In some embodiments, the analysis of platelet RNA content is performed by flow cytometry. The fold change in platelet RNA content can be compared to a baseline level prior to vaccination, and a sustained immune response is associated with at least about a 5-fold, at least about a 10-fold, or at least about a 20-fold increase compared to the baseline. In some embodiments, the platelets are CD3 + , CD8 + , CD20 + and CD14 + CD41 after cell exclusion + CD61+ In some embodiments, candidate vaccines or adjuvants are selected for development based on their ability to increase platelet RNA content, which is indicative of longer antibody longevity.

[0011] In other embodiments, methods are provided for determining whether a candidate adjuvant provides a core response specifically induced by a target high-potency reference adjuvant, e.g., AS03. Adjuvant use was predicted by using day 1 changes in expression of three of these genes, TGM2, ANKRD22, and KREMEN1. In some embodiments, methods are provided for selecting a candidate adjuvant with desirable properties, the methods comprising administering a vaccine containing the candidate adjuvant to a mammal, determining a core response signature from immune cells, e.g., peripheral blood mononuclear cells (PBMCs), and predicting whether the candidate adjuvant induces a core response by day 1 changes in expression. In some embodiments, the mammal is a mouse. In some embodiments, the mammal is a non-human primate. In some embodiments, the mammal is a human. In some embodiments, the candidate adjuvant is selected for development based on its ability to provide a high-potency core response on day 1 after vaccination.

[0012] A detailed multi-omics analysis of the cellular, transcriptional, and metabolic responses to the vaccine with or without adjuvant was performed, identifying a critical set of genes specifically induced by adjuvants in immune cells. Pathway analysis of these genes indicated a role for apoptosis in the adjuvant action mechanism, and plasma metabolomic analysis showed that adjuvant-induced perturbations in lipid and fatty acid metabolism were highly associated with the expression of an apoptotic signature. An early gene signature that could successfully predict antibody response longevity was obtained in a cohort of vaccine recipients. Subsequent single-cell profiling revealed differences in RNA content between platelets as a major driver of this signature, reflecting cell adhesion-associated persistence of the antibody response.

[0013] Differentially expressed genes (DEGs) following vaccination were determined, with the majority of DEGs observed at day 1 after prime and boost. To identify specific pathways activated in response to vaccination, a gene set enrichment analysis (GSEA) was performed on genes ranked by fold change after vaccination using a set of blood transcriptional modules (BTMs). Merging BTM enrichment scores according to high-level functional categories revealed that the adjuvant enhanced vaccination by increasing expression of a broad range of innate and adaptive immune cells and pathways at days 1 and 7 post-prime, with strong enrichment of BTMs associated with monocyte and dendritic cell (DC) activation at early time points after each immunization, whereas the day 7 response was primarily dominated by robust B cell and plasma cell transcriptional responses.

[0014] For transcriptional signatures associated with sustained antibody responses, GSEA was performed on genes ranked by correlation with residual antibody titers at day 100 / day 42. Expression of cell cycle-related modules at day 7 post-prime and cell adhesion / platelet activation-related modules at days 1-7 post-boost were associated with increased persistence. Specifically, genes within the platelet activation / actin binding module showed strong concordance in correlation with antibody persistence.

[0015] Both quantitative and qualitative transcriptional differences were observed in the innate immune response following prime and boost immunization with adjuvant. A blood transcriptional signature of cellular trafficking associated with a more sustained antibody response to adjuvant vaccination was identified and used to successfully predict antibody persistence. CITE-seq analysis revealed that attenuated antibody responders showed a much steeper decline in platelet RNA content following the second vaccination compared to more sustained responders.

[0016] By performing a meta-analysis of adjuvanted and non-adjuvanted vaccine datasets, a common set of genes specifically induced by the targeted adjuvant was identified. Day 1 changes in expression of three of these genes, TGM2, ANKRD22, and KREMEN1, were used to predict adjuvant use. Early transcriptional changes in the "core" genes were strongly associated with the frequency of activated Tfh cells in the periphery 7 days after vaccination, indicating that these genes are involved in mechanisms of immunogenicity. [Brief description of the drawings]

[0017] The invention is best understood from the following detailed description when read in conjunction with the accompanying drawings, in which: It is emphasized that, according to common practice, the various features of the drawings are not to scale. Conversely, dimensions of various features have been arbitrarily expanded or reduced for clarity.

[0018] [Figure 1]AS03 induces a strong early transcriptional signature that is boosted after booster vaccination. A shows an overview of the study. A total of 50 healthy subjects aged 21-45 years were randomized 2:1 to receive two doses of monovalent, split-virion, inactivated H5N1 clade 2.1 A / Indonesia / 05 / 2005 influenza vaccine administered with (n=34) or without (n=16) AS03 adjuvant, 21 days apart. Biological samples were collected and analyzed at regular intervals as indicated (grey squares). B shows the number of DEGs (p<0.01 and log2FC>0.2) after vaccination in adjuvanted (orange) and non-adjuvanted (green) subjects. C and D show the average blood transcriptional module (BTM) enrichment scores by cell type / pathway at day 1 (dark) and day 7 (light) after prime (C) and boost (D). E shows enrichment scores of interferon-associated BTMs on days 1-7 after prime (top) and boost (bottom). F shows scatter plots of fold change on day 1 (x-prime, y-boost) for differential expression (FDR<0.03) of BTMs in adjuvant subjects on day 1 between prime and boost. BTMs are color-coded as indicated in legend. Genes in BTM M111.1 in G; each "edge" (gray line) represents a co-expression relationship (as described in Li et al., Nat. Immunol. 2014) and colors represent fold change on day 1 after prime (left) and boost (right). H shows scatter plots of fold change on day 3 (x-prime, y-boost) for differential expression (FDR<0.03) of BTMs in adjuvant subjects on day 3 between prime and boost. BTMs are color-coded as indicated in the legend. Genes in BTM89.0 in I; each "edge" (gray line) represents a co-expression relationship and colors represent fold change at day 3 after prime (left) and boost (right). [Diagram 2]Adjuvanted H5N1 vaccination promotes protective H5 head-directed antibody responses with persistence associated with transcriptional signatures of cell migration. A shows microneutralization (MN) titers against the H5N1A / Indonesia vaccine strain in adjuvanted (orange) and non-adjuvanted (green) subjects. Geometric mean is shown in bold line, while shading is for geometric standard deviation (SD). B shows H5 head:stem IgG binding efficiency ratio measured by surface plasmon resonance (SPR). C shows fold change at day 42 post-vaccination in IgG affinity to H5 head (left) and stem (right) as measured by SPR. D shows heatmap of BTMs whose post-vaccination activity correlates with antibody persistence in both H5N1+AS03 (orange) and TIV (pink) vaccine responses. Gene set enrichment analysis (GSEA, FDR<0.05; 1,000 permutations) (Subramaniam et al. 2004) was used to identify positive (red), negative (blue), or no (gray) enrichment for BTMs in pre-ranked gene lists, and genes were ranked according to correlation with day 100 residuals and day 42 (H5N1+AS03) or day 180 and day 28 (TIV) antibody responses. Genes in BTM M196 in E and F; each "edge" (gray line) represents a co-expression relationship (as described in Li et al., Nat. Immunol. 2014), and colors represent correlation between day 7 gene expression and day 100 (H5N1+AS03, E) or day 180 (TIV, F) residual antibody responses (positive-red, negative-blue) vaccination. G shows a scatter plot of actual vs. predicted day 100 antibody response residuals in the CCHI dataset. A linear regression-based approach trained on transcriptional correlates of ongoing antibody responses at day 7 was used to generate predicted day 100 antibody response residuals in the H5N1+AS03 and TIV datasets. See Methods section for details. H shows a bar plot of genes from BTM selected by the predictive model in G whose expression at day 7 significantly correlates with antibody longevity (day 100 or day 180 response residuals) in the H5N1+AS03 and TIV datasets. Bars represent meta-correlation coefficients from both datasets.*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. [Diagram 3] CITEseq analysis reveals a platelet origin for the transcriptional signature of antibody persistence. A shows UMAP representation of PBMCs from all analyzed samples (n=12, 3 "continuous" and 3 "attenuated" responders, day 21 and day 28 samples from each subject) colored by manually annotated cell types. B shows UMAP representation of PBMCs from all analyzed samples showing the sum of expression per cell for all genes in the predicted signature of antibody persistence (Figure 2H). The left panel of C shows box plots of pseudo-bulk expression of the antibody persistence signature between continuing and attenuated responders. The right panel of C shows line graphs of the change in pseudo-bulk expression of the antibody persistence signature when a given cell cluster is excluded from the pseudo-bulk calculation. Groups were compared using one-tailed unpaired t-tests. D shows the platelet fraction of total cells in continuing and attenuated responders at days 21 and 28 after the first vaccination. E shows the percentage of total reads in platelet clusters in sustained and attenuated responders at days 21 and 28 after the first vaccination. F shows a bar graph of significantly enriched BTMBTM (FDR<0.05) by DEF over-representation test (p≦0.05&fc≧0.25) between sustained and attenuated responders in plasmablasts at day 28 after vaccination. G shows heatmap of genes in M238, M219, and M216 (left), M4.1 (middle), and M250 (right) modules. Colors represent row-wise z-score of mean expression in plasmablasts. [Figure 4]Circulating activated vaccine-induced Tfh cells correlate with neutralizing antibody titers and antibody affinity maturation. A shows the frequency of activated Tfh cells after vaccination in adjuvanted (orange) and non-adjuvanted subjects (green), defined as the percentage of PD1+ICOS+ cells within the CXCR5+CD4+ T cell population. B shows the correlation between day 28 / 21 fold change in activated Tfh cell frequency and day 42 / 21 fold increase in MN titers. C shows the correlation between day 28 / 21 fold change in activated Tfh cell frequency and day 42 / 21 fold increase in IgG affinity to H5 heads. D shows the mean log2 fold change of genes in activated (PD-1+ICOS+) cells versus non-activated (PD-1-ICOS-) Tfh1 (x-axis) and Tfh2 (y-axis) cells. The top 20 genes with the largest mean absolute fold change are annotated in red. E shows the estimated frequency of monocytes in non-activated and activated Tfh based on digital cytometry of transcriptional profiles using CIBERSORT. BTMs in F were significantly enriched (FDR<0.05) in activated vs non-activated Tfh cells. CIBERSORTx was used to estimate CD4 T cell specific expression in sorted Tfh transcriptional profiles, and then GSEA (Subramaniam et al. 2004) was used to identify enriched BTMs with genes ranked by their fold change between activated and non-activated Tfh. For details, see Methods section. In G, genes in BTM M219; each "edge" (grey line) represents a co-expression relationship (as described in Li et al., Nat. Immunol. 2014) and colors represent fold change in activated vs non-activated Tfh. In H, genes in BTM M4.2; each "edge" (grey line) represents a co-expression relationship and the color represents the fold change of activated vs. inactivated Tfh. I, Clustered heatmap of the top 40 genes by fold change between activated and inactivated Tfh. [Diagram 5]The initial molecular signature is associated with multiple markers of immune response after boost. A shows a schematic diagram illustrating the identified association between the gene signature on day 1 and the increase in activated Tfh frequency on day 28, IgG affinity to H5 head on day 42, and microneutralization titer on day 42. B shows a 3D scatter plot of the post-boost fold change of activated Tfh frequency (day 28), IgG affinity to H5 head (day 42), and microneutralization titer (day 42) in adjuvanted (orange) and non-adjuvanted (green) subjects. C shows a bar graph (FDR<0.05) of BTMs commonly associated with all three parameters (activated Tfh on day 28, H5 head IgG affinity on day 42, and MN titer on day 42). GSEA was performed on genes ranked by correlation with each parameter. D shows a 3D scatter plot of day 1 BTM association with the post-boost fold change of each immune parameter. Axes represent the GSEA enrichment scores of BTMs within the pre-ranked gene list, and genes were ranked according to their correlation with each immune parameter. E shows a heatmap of genes whose expression on day 1 is significantly correlated (p<0.001) with all three immune parameters. Colors represent fold change from day 1 / day 0. [Figure 6]A meta-analysis of influenza vaccine trials reveals an AS03-specific transcriptional signature that correlates with activated Tfh cell frequency in the periphery. A shows the identification of the AS03-specific gene signature. Data from both the prime and boost doses of this trial, as well as publicly available data from previous studies of responses to AS03-adjuvanted H1N1 vaccination, were incorporated and compared pairwise with gene expression data from multiple TIV trials. For details, see the Methods section. B shows a heatmap of differentially expressed genes (p<0.005 and log2FC>0.2) in all pairwise comparisons between AS03 adjuvanted and non-adjuvanted influenza vaccine studies. Of these, three "core" genes (ANKRD22, KREMEN1, and TGM2) shared a large number of co-correlated genes. C shows predicted adjuvant status of adjuvanted (orange) and non-adjuvanted (green) vaccine recipients in independent studies. An artificial neural network-based machine learning classification algorithm was trained to predict vaccine status (adjuvanted vs. non-adjuvanted) using day 1 expression data of "core" AS03-specific genes within this study, and to predict vaccine status (adjuvanted vs. non-adjuvanted) in the absence or presence of AS03. We tested on an independent dataset containing expression data from a previously published study of response to H5N1 vaccination. The dot plots display the results of 10 randomized bootstrap tests, with each dot representing the ensemble vote of classifiers (1 - adjuvant, 0 - non-adjuvant) within a single test. For details, see Methods section. D shows the classification accuracy of the "core" AS03 gene classifier trained within this study and applied to two independent datasets. For details, see Methods section. E shows the correlation between day 22 expression of the three "core" AS03-specific genes and day 28 / 21 fold change in activated Tfh cell frequency. F shows the correlation between day 1 expression of TMEM159 genes and day 42 / 0 fold change in MN titers.G shows the correlation between the expression of the TMEM159 gene on day 1 and the fold change of MN titer on days 63 / -7 in a publicly available dataset where AS03 was co-administered with the monovalent H1N1 vaccine. [Figure 7] The generation of initial apoptotic signals after AS03 vaccination is associated with disturbances in fatty acid metabolism and oxidation. A and B show the differentially expressed genes (FDR < 0.05) in the apoptotic-related pathways (Reactome database) between the AS03 + H5N1 group on day 1 (A) and day 22 (B) after vaccination and the H5N1 and TIV datasets (pairwise comparison). Genes belonging to specific apoptotic sub-pathways are color-coded in green (intrinsic pathway), magenta (extrinsic pathway), blue (execution phase), or orange (regulation). C shows the metabolic trajectories after priming along the first two principal components for adjuvant and non-adjuvant subjects relative to day 0. Here, the metabolic trajectories refer to the trajectories of each subject following the change in abundance over all differentially expressed metabolite features (p < 0.01) over time (days 1 - 7) when projected onto the principal component space. D shows the metabolic trajectories after boost along the first two principal components for adjuvant and non-adjuvant subjects relative to day 21. E shows the metabolic pathways significantly enriched (p < 0.05) in adjuvant subjects on day 1 after vaccination. The size of the circles represents the number of abnormally abundant metabolites detected within the pathway. F shows a heatmap of metabolic pathways related to apoptotic gene expression or immune response after boost on day 22. Enriched pathways were identified using Mummichog software based on metabolite features correlated (p < 0.05 by Spearman correlation) with apoptotic gene expression or immune response. The color and size of the circles represent the -log10 p-value of significant pathway enrichment (p < 0.1) by permutation test. The pathways shown are those with enrichment < p-value 0.01 and having at least one feature. [Figure 8]A shows a scatter plot of the mean log2 FC of all BTMs in adjuvanted subjects on day 1 (x-axis) and non-adjuvanted subjects on day 3 (y-axis). B shows the kinetics of differential BTMs between prime and boost on day 3. The lines represent the mean module fold change between adjuvanted subjects. The 10 BTMs with the largest fold change on day 24 are plotted (same as those labeled in FIG. 1G). C shows hemagglutination inhibition (HAI) titers against the H5N1A / Indonesia vaccine strain in adjuvanted (orange) and non-adjuvanted (green) subjects. The geometric mean is shown in bold, while the shading is for the geometric standard deviation (SD). D shows MN titers against heterologous clade 2 H5N1 strains. The geometric mean is shown in bold, while the error bars represent the geometric standard deviation (SD). E shows H5 head and stem IgG binding capacity in resonance units (RU) as measured by surface plasmon resonance (SPR). Medians and interquartile ranges are shown in box plots and violin plots show sample distribution. F shows correlation of fold change in IgG antibody binding to H5 head on day 42 / 21 and with MN titer. G shows correlation of fold change in IgG affinity to H5 head on day 42 / 21 and with MN titer. [Figure 9] A shows the kinetics of HAI titers in response to H5N1+AS03 and TIV vaccination. Lines represent geometric means and shaded areas represent geometric standard deviations. TIV titers are from young adults (<65 years) vaccinated with 2010 and 2011 seasonal influenza vaccines (n=42) (Nakaya et al., 2015). B shows correlation of HAI titers at day 100 and day 42. C shows heatmap of mean log2 FC of plasma cells and cell cycle BTMs in adjuvanted subjects. D shows scatter plot of mean log2 FC (x-axis) of M156.0 at day 28 / day 21 vs. day 100 / day 42 HAI residuals in adjuvanted subjects. [Figure 10]A shows the cell percentage per cluster of all analyzed cells before QC filtering. The left panel shows the percentage of cells in each cluster from day 21 and day 28 samples. The right panel shows the percentage of cells in each cluster from each subject. B shows a scatter plot of the day 28 / 21 FC of day 28 DEGs via microarray (x-axis) and pseudo-bulk estimates via CITEseq (y-axis) for each subject. Statistics were generated using Pearson correlation. C shows box plots of day 28 / 21 FC in platelets of antibody persistence signature genes for each subject using normalized expression per cell. [Figure 11] A shows QC metrics per cell by cluster before QC filtering. B shows DEGs in each cluster before QC filtering compared to all other clusters. C shows CITE-seq antibody abundance in each cell before QC filtering. [Figure 12] A shows the gating strategy for sorting of four distinct CD4+CXCR5+Tfh populations: resting Tfh1, resting Tfh2, activated Tfh1, and activated Tfh2. B shows the NES (Newman et al. 2019) of BTMs significantly enriched (FDR<0.05 and NES>2.5) in sorted activated vs. non-activated Tfh cells prior to deconvolution using CIBERSORTx. Genes ranked by their average fold change between sorted activated and non-activated Tfh samples using GSEA (Subramaniam et al. 2004) were used to identify enriched BTMs. C shows the estimated relative cellular fractions (Newman et al. 2015) of various immune cells in Tfh samples sorted via CIBERSORT. [Figure 13]A shows a heatmap of BTMs that were commonly enriched (FDR<0.001) in response to both TIV and H5N1+AS03 (prime or boost) on days 1 or 7 post-vaccination. Colors represent NES, with non-significant scores shaded gray. B shows the difference in mean NES between the AS03 and TIV datasets for BTMs that were uniquely enriched in the AS03 dataset. GSEA (see Subramanian et al., 2005) was used to identify enrichment of BTMs using ranked gene lists, where genes were t-statistically ordered based on fold change from day 1 to day 0 in the AS03 and TIV datasets (see FIG. 5A and Methods). BTMs shown are those that were significantly enriched (FDR<0.05) in all AS03 datasets but not in the TIV dataset. C shows genes in BTM M23. Each "edge" (gray line) represents a co-expression relationship and the color represents the average fold change at day 1 across all AS03 or TIV studies (see FIG. 5A and Methods). D shows the overlap coefficient of significantly correlated partner genes (FDR<0.1) at day 1 among all "AS03-specific" genes. E shows the estimated gene expression of "AS03-specific" genes in different cell types at day 1 for the adjuvant groups. [Figure 14]Positive correlation between fold change in platelet RNA content at day 7 vs. day 0 after last immunization and persistence of antibody response in human, rhesus and mouse studies. A shows experimental timeline of platelet staining and HAI assay in seasonal influenza vaccination. Subjects were vaccinated with one dose of TIV (Fluzone®, Sanofi Pasteur Inc., 2010-11 season). B shows scatter plot of day 180 / day 28 HAI residual against day 7 / day 0 log2 fold change in total platelet RNA content (median RNA dye intensity) and % RNA+ platelets in vaccinated subjects. C shows experimental timeline of platelet staining and neutralization assay in rhesus macaques immunized with hexamers of RBD or SARS-CoV-2 spike immunogen mixed with AS03. D shows line graph of kinetics of SARS-CoV2019 virus neutralizing antibody titers in serum. E shows scatter plots of nAb remaining at day 180 / day 42 versus day 28 (d7) / baseline (d0) log2 FC of RNA content in total platelets and %RNA+platelets in adjuvanted subjects. F shows experimental timeline of platelet staining, neutralization assay, and EPISPOT in rhesus macaques immunized with 1086.C gp140 immunogen from HIV-1 clade C mixed with R848 or MPL (TLR-4 targeting monophosphoryl lipid A)+R848. G shows line graphs of kinetics of tier 1A MW965.26 HIV-1 pseudovirus neutralizing antibody titers in serum. H and I show scatter plots of week 19 (d7) / baseline (d0) log2 FC of RNA content in total platelets versus week 42 / week 20 nAb remaining and bone marrow ASC numbers in adjuvanted subjects. J shows experimental timeline of platelet staining, ELISA, and ELISPOT in C57BL / 6J mice immunized with SARS-CoV-2 (2019-nCoV) spike immunogen mixed with AS03. K shows line graphs of kinetics of anti-spike binding antibody titers in serum. L and M show scatter plots of day 7 / day 0 log2FC of RNA content in whole platelets versus remaining day 42 / day 7, and bone marrow ASC numbers. [Figure 15]A, D, F, and H show gating strategies for acellular platelets in thawed PBMCs of human subjects in 2010 seasonal influenza vaccination (A), rhesus macaques immunized with SARS-CoV-2 spike immunogen mixed with AS03 (D), rhesus macaques immunized with HIV gp140 immunogen mixed with R848 or MPL+R848 (F), or acellular platelets in freshly prepared platelet-enriched plasma of B6 mice immunized with 2019-n-CoV spike immunogen mixed with AS03 (H). B shows % platelets on days 0 and 7 in the gated populations, and the fold change in % platelets on day 7 vs. day 0. C, E, G, and I show the fold change in RNA content (median RNA dye intensity) or % RNA+ platelets in total platelets in vaccinated subjects on day 7 after the last immunization vs. the day of last immunization (C, I) or baseline (E, G).

[0019] (Table 1) Demographic information for the 50 subjects enrolled in the two treatment arms of this study is shown. Table 2 shows the geometric mean titers (GMT), 95% confidence intervals (CI), and seroconversion rates for HAI and MN titers for both the non-adjuvanted and AS03-adjuvanted groups. Seroconversion rates are defined as the proportion of vaccinees with a 4-fold or greater increase in titer relative to baseline levels after vaccination. JPEG2024523386000002.jpg180170 DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Compositions and methods are provided for classification of vaccines, particularly including vaccine adjuvants, for persistence of quality of response based on changes in gene expression at early time points after vaccination. The pattern of response is obtained by quantifying the signal in immune cell subsets of interest after a period of 1-10 days after vaccination, including, for example, day 7 after vaccination. The pattern of response shows a tendency to have a benchmarked response against a reference adjuvant and shows a longevity of antibody response of 100 days or more. Once classification is made, it can be used to select and benchmark vaccines for therapeutic use. Classification can further include selection of agents or regimens.

[0021] Before the present methods and compositions are described, it is to be understood that this invention is not limited to the particular methods or compositions described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0022] Where a range of values ​​is provided, it is to be understood that each intervening value between the upper and lower limit of that range is also specifically disclosed to the tenth of the unit of the lower limit, unless the context clearly dictates otherwise. Each smaller range between any stated or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the range, and each of the ranges in which the smaller range includes either of the limits, neither of the limits, or both limits are included is also encompassed within the invention, subject to any specifically excluded limits in the stated range. Where a stated range includes one or both of the limits, ranges excluding either or both of those included limits are likewise included in the invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential preferred methods and materials are described herein. All publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. In case of conflict, it should be understood that the present disclosure supersedes any disclosure of the incorporated publications.

[0024] It must be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells, and reference to "the peptide" includes reference to one or more peptides and equivalents thereof known to those skilled in the art, such as polypeptides.

[0025] Publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present invention is not entitled to antedate such publications by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0026] The term "adjuvant" generally refers to a composition that increases an individual's humoral or cellular immune response. A contemplated adjuvant stimulates the immune system and increases responsiveness or duration of response to a co-administered antigen.

[0027] The terms "subject," "individual," and "patient" are used interchangeably herein to refer to a mammal being evaluated for a response. In some embodiments, the mammal is a human. The terms "subject," "individual," and "patient" include, but are not limited to, individuals with a disease. The subject may be a human, but also includes other mammals, particularly mammals useful as laboratory models for human disease, e.g., mice, rats, and the like. The methods of the invention can be applied for veterinary purposes.

[0028] As used herein, the term "theranosis" refers to the use of results obtained from a diagnostic method to direct the selection, maintenance, or modification of a treatment regimen, including, but not limited to, selection of one or more therapeutic agents, changes in dose levels, changes in dose schedules, changes in mode of administration, and changes in formulation. Diagnostic methods used to inform theranosis can include any method that provides information regarding the status of a disease, condition, or symptom.

[0029] The terms "therapeutic agent", "therapeutic agent", or "therapeutic agent" are used interchangeably and refer to a molecule or compound that confers some beneficial effect upon administration to a subject, including vaccines and vaccine adjuvants. Beneficial effects include the induction of a therapeutic immune response, the viability of diagnostic determinations, the amelioration of a disease, symptom, disorder, or pathological condition, the reduction or prevention of the onset of a disease, symptom, disorder, or condition, and generally the counteracting of a disease, symptom, disorder, or pathological condition.

[0030] As used herein, "treatment" or "treat" or "alleviate" or "ameliorate" are used interchangeably. These terms refer to an approach to obtain beneficial or desired results, including, but not limited to, therapeutic benefit and / or preventive benefit. Therapeutic benefit refers to any therapeutically relevant improvement or effect in one or more diseases, conditions, or symptoms being treated. For preventive benefit, the composition may be administered to a subject at risk of developing a particular disease, condition, or symptom, or a subject reporting one or more of the physiological symptoms of a disease, even if the disease, condition, or symptom may not yet be manifested.

[0031] The term "effective amount" or "therapeutically effective amount" refers to an amount of an agent sufficient to produce a beneficial or desired result. The therapeutically effective amount varies depending on the subject and disease state being treated, the weight and age of the subject, the severity of the disease state, the method of administration, etc., which can be easily determined by one of ordinary skill in the art. The term also applies to a dose that provides an image for detection by any one of the imaging methods described herein. The specific dose will vary depending on the particular agent selected, the dosing regimen to be followed, whether it is administered in combination with other compounds, the timing of administration, the tissue to be imaged, and the physical delivery system in which it is delivered.

[0032] "Favorable conditions" shall have a meaning that depends on the context in which the term is used. That is, when used in relation to an antibody, the term shall mean a condition that allows the antibody to bind to its corresponding antigen. When used in relation to contacting an agent with a cell, the term shall mean a condition that allows the agent capable of doing so to enter the cell and perform its intended function. In one embodiment, as used herein, the term "favorable conditions" refers to physiological conditions.

[0033] The term "inflammatory" response is the development of a humoral (antibody-mediated) and / or cellular response, which may be mediated by antigen-specific T cells or their secretory products, and innate immune cells. An "immunogen" is capable of inducing an immune response against itself upon administration to a mammal, or due to an autoimmune disease.

[0034] The term "vaccine" as used herein is defined according to the related art and relates to a composition that induces or enhances protective immunity in an individual against a particular disease caused by a pathogen. Without wishing to be bound by theory, it is believed that protective immunity results from the generation of neutralizing antibodies or the activation of cytotoxic cells of the immune system, or both. To induce or enhance protective immunity, a vaccine contains as an immunogenic antigen a part of the pathogen causing the disease or a nucleic acid molecule encoding this immunogenic antigen. Upon contact with the immunogenic antigen, the immune system of the individual is triggered to recognize the immunogenic antigen as foreign and to destroy it. The immune system then remembers the contact with this immunogenic antigen, ensuring easy and efficient recognition and destruction of the pathogen on subsequent contact with the disease-causing pathogen.

[0035] Vaccines known and used in the art include, for example, inactivated pathogen vaccines, attenuated live pathogen vaccines, messenger RNA (mRNA) vaccines, subunit, recombinant, polysaccharide, and conjugate vaccines, toxoid vaccines, and viral vector vaccines. Inactivated vaccines use killed forms of disease-causing pathogens, such as, for example, Hepatitis A, influenza, and rabies. Live vaccines use attenuated forms of disease-causing pathogens, such as measles, mumps, rubella (MMR combination vaccine), rotavirus, smallpox, chickenpox, and yellow fever. mRNA vaccines encode pathogen proteins that elicit an immune response, such as SRS-CoV2. Subunit, recombinant, polysaccharide, and conjugate vaccines use specific pathogen molecules, such as Hib (Haemophilus influenzae type b), Hepatitis B, HPV (Human papillomavirus), Bordetella pertussis, Pneumococcal disease, Meningococcal disease, and Herpes zoster virus. Toxoid vaccines use toxins formed by pathogens such as diphtheria and tetanus. Viral vector vaccines use modified versions of different viruses as vectors to deliver sequences that code for pathogen proteins. Several different viruses have been used as vectors, including influenza, vesicular stomatitis virus (VSV), measles virus, and adenovirus. Viral vectors are currently being used for SARS-CoV2 vaccination.

[0036] For the purposes of the present invention, the term "biomarker" or "marker" refers to, but is not limited to, proteins with their associated metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analyte or sample-derived metrics. Markers include expression levels of genes of interest. Markers may also include combinations of any one or more of the above measurements, including time trends and differences. When used broadly, markers may also refer to immune cell subsets.

[0037] "Analyzing" includes determining a set of values ​​associated with a sample by measuring a marker in a sample (e.g., the presence or absence of a marker or a constitutive expression level) and comparing the measurements to measurements in a sample or set of samples from the same subject or other control subjects. The markers of the present teachings may be analyzed by any of a variety of conventional methods known in the art. "Analyzing" may include performing statistical analysis, such as normalizing data, determining statistical significance, determining statistical correlation, clustering algorithms, and the like.

[0038] A "sample" in the context of the present teachings refers to any biological sample isolated from a subject, generally a sample containing circulating immune cells. Samples may include, but are not limited to, bodily fluids, whole blood, PBMCs (white blood cells or leukocytes), tissue biopsies, synovial fluid, lymphatic fluid, peritoneal fluid, and aliquots of interstitial or extracellular fluid. A "blood sample" may refer to whole blood or a portion thereof, including blood cells, white blood cells, or leukocytes. Samples may be obtained from a subject by means including, but not limited to, venipuncture, biopsy, needle aspiration, lavage, scraping, surgical incision or intervention, or other means known in the art.

[0039] A "dataset" is a set of numerical values ​​obtained from the evaluation of a sample (or a population of samples) under desired conditions. The values ​​of a dataset can be obtained, for example, by experimentally obtaining evaluation criteria from the samples and constructing a dataset from these measurements, or alternatively, by obtaining the dataset from a service provider, such as a laboratory, or from a database or server on which the dataset is stored. Similarly, the term "obtaining a dataset associated with a sample" encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample and processing the sample to experimentally determine the data, for example, via measuring antibody binding or other methods to quantify a signaling response. This phrase also encompasses receiving a set of data, for example, from a third party who has processed the sample to experimentally determine the dataset.

[0040] "Measuring" or "measurement" in the context of the present teachings refers to determining the presence, absence, quantity, amount, or effective amount of a substance, including the presence, absence, or concentration level of such a substance in a clinical or subject-derived sample, and / or assessing the value or classification of a subject's clinical parameter based on a control, e.g., a baseline level of a marker.

[0041] Classification can be performed according to a predictive modeling method that sets a threshold value to determine the probability that a sample belongs to a given class. The probability is preferably at least 50%, or at least 60%, or at least 70%, or at least 80% or more. Classification can also be performed by determining whether a comparison between the obtained data set and a reference data set results in a statistically significant difference. If so, the sample from which the data set was obtained is classified as not belonging to the reference data set class. Conversely, if such a comparison is not statistically significantly different from the reference data set, the sample from which the data set was obtained is classified as belonging to the reference data set class.

[0042] The predictive ability of a model can be evaluated according to its ability to provide a quality metric of a particular value or range of values, such as AUC or accuracy. In some embodiments, the desired quality threshold is a predictive model that classifies samples with an accuracy of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or more. As an alternative evaluation criterion, the desired quality threshold can refer to a predictive model that classifies samples with an AUC (area under the curve) of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or more.

[0043] As is known in the art, the relative sensitivity and specificity of a predictive model can be "tuned" to favor either the selectivity metric or the sensitivity metric, the two metrics having an inverse relationship. The limits of such models can be adjusted to provide a selected sensitivity or specificity level depending on the particular requirements of the test being performed. One or both of the sensitivity and specificity can be at least about at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or more.

[0044] "Affinity reagent" or "specific binding member" may be used to refer to an affinity reagent, such as a polynucleotide, an antibody, a ligand, etc., that selectively binds to a gene sequence, protein, or marker of the invention. The term "affinity reagent" includes any molecule, e.g., a peptide, a nucleic acid, a small organic molecule. In some embodiments, the affinity reagent is a polynucleotide.

[0045] The term "antibody" includes full length antibodies and antibody fragments, and may refer to natural antibodies from any organism, engineered antibodies, or antibodies recombinantly generated for experimental, therapeutic, or other purposes, as further defined below. Examples of antibody fragments known in the art, such as Fab, Fab', F(ab')2, Fv, scFv, or other antigen-binding subsequences of antibodies, are either produced by modification of whole antibodies or synthesized de novo using recombinant DNA technology. The term "antibody" includes monoclonal and polyclonal antibodies. Antibodies may be antagonists, agonists, neutralizers, inhibitors, or stimulators. They may be humanized, glycosylated, attached to solid supports, and have other modifications.

[0046] This invention incorporates information disclosed in other applications and texts. The following patents and other publications are incorporated herein by reference in their entirety: Alberts et al., The Molecular Biology of the Cell, 4th Ed., Garland Science, 2002; Vogelstein and Kinzler, The Genetic Basis of Human Cancer, 2d Ed., McGraw Hill, 2002; Michael, Biochemical Pathways, John Wiley and Sons, 1999; Weinberg, The Biology of Cancer, 2007; Immunobiology, Janeway et al. 7th Ed., Garland, and Leroith and Bondy, Growth Factors and Cytokines in Health and Disease, A Multi Volume Treatise, Volumes 1A and IB, Growth Factors, 1996.

[0047] Unless otherwise clear from the context, all elements, steps or features of the invention can be used in any combination with other elements, steps or features.

[0048] General methods in molecular and cellular biochemistry are Molecular Cloning: A Laboratory Manual, 3rd Ed. (Sambrook et al., Harbor Laboratory Press 2001), Short Protocols in Molecular Biology, 4th Ed. (Ausubel et al. eds., John Wiley & Sons 1999), Protein Methods (Bollag et al., John Wiley & Sons 1996), Nonviral Vectors for Gene Therapy (Wagner et al. eds., Academic Press 1999), Viral Vectors (Kaplift & Loewy eds., Academic Press 1995), Immunology Methods Manual (I. Lefkovits ed., Academic Press 1997), and Cell and Tissue Culture: Laboratory Procedures in Biotechnology (Doyle & Griffiths, John Wiley & Sons 1998). Reagents, cloning vectors, and kits for genetic manipulation referred to in this disclosure are available from commercial vendors such as BioRad, Stratagene, Invitrogen, Sigma-Aldrich, and ClonTech.

[0049] The present invention has been described with respect to specific embodiments found or provided by the inventors to include preferred modes for carrying out the invention. Those skilled in the art will understand in light of this disclosure that numerous modifications and changes can be made in the specific embodiments exemplified without departing from the intended scope of the invention. By considering biological functional equivalence, changes in protein structure can be made without affecting biological action in kind or amount. All such modifications are intended to be within the scope of the appended claims.

[0050] The method of the present invention can be used for preventive or therapeutic purposes. As used herein, the term "treat" is used to refer to both the prevention of recurrence and the treatment of an existing condition. For example, immune development can be achieved by administration of a drug. Of particular interest is the treatment of ongoing disease, where treatment stabilizes or improves the patient's clinical symptoms.

[0051] Methods of the Invention Analysis of cellular biological samples obtained from individuals at the single cell or multi-cell level is used to obtain a determination of changes in immune cell gene expression associated with immunization. Surprisingly, it is found that changes in gene expression of these immune cells predict the propensity to develop a sustained antibody response to a vaccine. In some embodiments, the immune cells are platelets.

[0052] The sample may be of any suitable type that allows for the analysis of one or more cells, preferably a blood sample, a PBMC sample, or a fraction thereof, e.g., platelets. The sample may be obtained from an individual once or multiple times. Multiple samples may be obtained from different locations within the individual (e.g., a blood sample, a bone marrow sample, and / or a lymph node sample), from the individual at different times, or any combination thereof. In one embodiment, a baseline, or "day 0" sample is obtained prior to immunization, and a test sample is obtained about 7 to about 10 days after immunization, e.g., about day 7, about day 8, about day 9, about day 10, and may be about day 6 to about day 11, about day 7 to about day 10, about day 7 to about day 9, about day 7 to about day 8.

[0053] If the samples are obtained as a series, for example a series of blood samples obtained during that time, the samples can be obtained at fixed intervals, with intervals determined by the state of the most recent sample(s), or by other characteristics of the individual, or by some combination thereof. The intervals may not be strict according to the availability of the individual for sampling and the availability of sampling equipment, and thus it is understood that approximate intervals corresponding to the intended spacing scheme are encompassed by the present invention. In general, the most easily obtained samples are fluid samples. In some embodiments, the sample is blood.

[0054] One or more cells or cell types, or a sample containing one or more cells or cell types, can be isolated from a body sample. Cells can be separated from a body sample by red blood cell lysis, centrifugation, elution, density gradient separation, apheresis, affinity selection, panning, FACS, centrifugation through Hypaque, solid support with bound antibodies (magnetic beads, beads in a column, or other surfaces), etc. By using antibodies specific to markers identified on a particular cell type, a relatively homogeneous cell population can be obtained. Alternatively, a heterogeneous cell population can be used, for example, circulating peripheral blood mononuclear cells.

[0055] In some embodiments of the invention, specific cell populations (e.g., CD4 + Different gating strategies are used to analyze only T cells, only platelets, etc. These gating strategies can be based on the presence of one or more specific surface markers. The following gates can distinguish between dead and live cells, and the subsequent gating of live cells classifies them into, for example, myeloblasts, monocytes, and lymphocytes. Unequivocal comparisons can be made by using two-dimensional contour plot representations, two-dimensional dot plot representations, and / or histograms.

[0056] Samples can be obtained at one or more time points. When a single time point sample is used, it is compared to a reference "baseline" level for the presence of the activated form of the signaling protein of interest, which can be obtained from a normal control, a pre-determined level obtained from an individual or population of individuals, a negative control for ex vivo activation, etc.

[0057] If necessary, the cells are dispersed into a single cell suspension, for example by enzymatic digestion with a suitable protease, such as collagenase, dispase, and the like. An appropriate solution is used for dispersion or suspension. Such solutions are generally balanced salt solutions, such as normal saline, PBS, Hank's balanced salt solution, and the like, conveniently supplemented with fetal bovine serum or other natural factors, along with an acceptable buffer at low concentration, generally 5-25 mM. Convenient buffers include HEPES1 phosphate buffer, lactate buffer, and the like. The cells can be fixed, for example, with 3% paraformaldehyde, and typically permeabilized, for example, by covering with ice-cold methanol, HEPES-buffered PBS containing 0.1% saponin, 3% BSA, acetone at -200°C for 2 minutes, as known in the art and according to the methods described herein.

[0058] In one embodiment, a method for predicting the durability of an immune response to a candidate vaccine is provided, the method comprising administering the candidate vaccine, which may include an adjuvant, to a mammal and determining the RNA content of platelets in the recipient following vaccination.

[0059] In some embodiments, the analysis of platelet RNA content is carried out by one-step flow cytometry analysis, for example, fluorescence-activated flow cytometry.In such a method, a sample, for example, a peripheral blood sample, is labeled with a reagent that can distinguish platelets from other cells in the sample, and is labeled with an RNA-selective stain.The population of cells is then analyzed by flow cytometry, gated on the platelet population, and the RNA content of platelets is determined.The cells may be fresh or frozen, or may be fixed before analysis.

[0060] In such embodiments, a sample from an individual is contacted with a directly or indirectly labeled binding agent, e.g., one or a cocktail of labeled antibodies, specific for a marker that can distinguish platelets. In some embodiments, the binding agent is specific for CD41 and CD61, and platelets are specific for CD41. + CD61 + In some embodiments, the cocktail of binding agents further comprises agents specific for one or more of CD3, CD8, CD14, CD19, CD20, CD56, etc., and these markers are used to exclude non-platelet cells from the analysis. For example, a cocktail of antibodies for staining may comprise detectably labeled anti-CD3, anti-CD19, anti-CD14, anti-CD56, anti-CD41, and anti-CD61 antibodies. Another cocktail of antibodies may comprise anti-CD3, anti-CD8, anti-CD20, anti-CD14, anti-CD41, and anti-CD61 antibodies.

[0061] Alternatively, a blood sample may be anticoagulated to obtain platelet-rich plasma, which is contacted with a directly or indirectly labeled binding agent, e.g., one or a cocktail of labeled antibodies, specific for a marker that can distinguish platelets. In some embodiments, the binding agent is specific for CD41 and CD61, and platelets are bound to CD41. + CD61 +In some embodiments, the cocktail of binding agents further comprises an agent specific for one or more of the red blood cell markers, including but not limited to TER119, which is used to exclude non-platelet RBCs from the analysis. For example, the cocktail of antibodies for staining may comprise detectably labeled anti-TER119, anti-CD41, and anti-CD61 antibodies.

[0062] The sample is contacted with a dye that is selective for RNA. Suitable dyes for this purpose are commercially available, such as RNASelect™ Stain (Invitrogen), which exhibits bright green fluorescence when bound to RNA (absorption / emission maxima of about 490 / 530 nm), but only a weak fluorescent signal when bound to DNA. Other RNA-selective dyes are known in the art, such as the styryl dyes E36, E144 and F22 described by Li et al. (2006) Chemistry and Biology 13(6):615-623 (specifically incorporated herein by reference), and the RNA-selective fluorescent dyes integrated with thiazole orange and p-(methylthio)styryl moieties described by Lu et al. Chemical Communications 215(83).

[0063] The samples were then analyzed for CD41 + CD61 + Platelets are gated, optionally excluding RBCs and other immune cells, and analyzed by flow cytometry to determine platelet RNA content. The fold change in platelet RNA content can be compared to pre-vaccination baseline levels, and a sustained immune response is associated with at least about a 5-fold, at least about a 10-fold, or at least about a 20-fold increase compared to baseline.

[0064] Data analysis Signature patterns can be generated from biological samples using any convenient protocol, for example, as described below. The readout can be the mean, average, median, or variance, or other statistically or mathematically derived values ​​associated with the measurements, such as gene expression, RNA content, etc. The marker readouts can be further scrutinized by direct comparison with the corresponding reference or control patterns. The signatures can be evaluated at several points to determine whether there is a statistically significant change at any point in the data matrix compared to the reference value, whether the change is an increase or decrease in binding, whether the change is specific to one or more physiological conditions, etc. The absolute values ​​obtained for each marker under identical conditions reflect the inherent variability of the biological system and also the inherent variability between individuals.

[0065] After obtaining a signature pattern from the sample to be assayed, the signature pattern can be compared to a reference or baseline profile and a classification can be made regarding the response of the patient from whom the sample was obtained / derived. Additionally, the reference or control signature pattern can be a signature pattern obtained from a sample of a reference adjuvant.

[0066] In certain embodiments, the obtained signature pattern is compared to a single reference / control profile to obtain phenotypic information. In yet other embodiments, the obtained signature pattern is compared to two or more different reference / control profiles to obtain more detailed depth information. For example, the obtained signature pattern is compared to positive and negative reference profiles to obtain confirmatory information.

[0067] The sample may be obtained from a tissue or bodily fluid of an individual. For example, the sample may be obtained from whole blood, tissue biopsy, serum, etc. The term also includes derivatives and fractions of such cells and bodily fluids.

[0068] To identify a profile, statistical tests can provide a confidence level for a change in the level of a marker between a test profile and a reference profile to be considered significant. The raw data can be first analyzed by measuring the value of each marker, usually in two, three, four, or five to ten replicates per marker. A test data set is considered different from a reference data set if one or more of the profile's parameter values ​​exceed a limit value corresponding to a predefined level of significance.

[0069] To provide the order of significance, a false discovery rate (FDR) can be determined. First, a set of null distributions of dissimilarity values ​​is generated. In one embodiment, the observed profile values ​​are permuted to form a sequence of distributions of correlation coefficients obtained by chance, thereby forming a suitable set of null distributions of correlation coefficients. The set of null distributions is obtained by permuting the values ​​of each profile for all available profiles, calculating pairwise correlation coefficients for all profiles, calculating the probability density function of the correlation coefficients for this permutation, and repeating the procedure N times (N is a large number, typically 300). The N distribution is used to calculate a suitable metric (mean, median, etc.) of the count of correlation coefficient values ​​that exceeds a value (similarity value) obtained from the distribution of experimentally observed similarity values ​​at a given significance level.

[0070] The FDR is the ratio of the expected number of spuriously significant correlations (estimated from correlations greater than this selected Pearson correlation in a set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cutoff correlation value can be applied to correlations between experimental profiles.

[0071] For SAM, the Z-score represents another measure of dispersion in a data set and is equal to X minus the mean of X divided by the standard deviation. The Z-score indicates how a single data point compares to a normal data distribution. The Z-score indicates not only whether a data point is above or below the mean, but also how unusual a measurement is. The standard deviation is the average distance between each value in a data set and the mean of the values ​​in the data set.

[0072] Using the aforementioned distribution, a confidence level is selected for significance. This is used to determine the minimum value of the correlation coefficient above what would have been obtained by chance. Using this method, a threshold for positive correlation, negative correlation, or both is obtained. Using this threshold, the user can filter the pairwise correlation coefficient observations to exclude those that do not exceed the threshold. Furthermore, for a given threshold, an estimate of the false positive rate can be obtained. For each of the individual "random correlation" distributions, it can be found how many observations are outside the threshold range. This procedure provides a sequence of counts. The mean and standard deviation of the sequence provide the average number of potential false positives and their standard deviation. Alternatively, any convenient statistical validation method can be used.

[0073] The data can be subjected to unsupervised hierarchical clustering to reveal relationships between profiles. For example, hierarchical clustering can be performed, where Pearson correlation is employed as the clustering metric. One approach is to consider the patient-disease dataset as a "training sample" in a "supervised learning" problem. CART is a standard in medical applications (Singer (1999) Recursive Partitioning in the Health Sciences, Springer), which can be modified by converting any qualitative features into quantitative features, sorting them by the significance level achieved, and performing Hotelling's T 2It is evaluated by suitable application of sample reuse and lasso methods for statistics. The prediction problem is transformed into a regression problem without losing sight of the prediction by appropriate use of the Gini criterion for classification in assessing the quality of the regression.

[0074] Other analytical methods that may be used include logistic regression. One method of logistic regression is Ruczinski (2003) Journal of Computational and Graphical Statistics 12:475-512. Logistic regression is similar to CART in that its classifier can be viewed as a binary tree. It differs in that each node has a Boolean statement about the function that is more general than the simple "and" statements produced by CART.

[0075] Another approach is that of nearest shrinking centroids (Tibshirani (2002) PNAS 99: 6567-72). This technique is similar to k-means, but has the advantage of automatically selecting features (like a lasso) by shrinking cluster centers, focusing attention on a small number of informative features. This approach is available as a software "plug-in" for Microsoft Excel, the Predictive Analysis of Microarrays (PAM) software, and is widely used. Two further sets of algorithms are Random Forests (Breiman (2001) Machine Learning 45: 5-32 and MART (Hastie (2001) The Elements of Statistical Learning, Springer). These two methods are already "committee methods"; thus, they include predictors that "vote" on the outcome. Some of these methods are based on the "R" software developed at Stanford University, which provides a statistical framework that is continuously improved and updated.

[0076] Other statistical analysis approaches include principal component analysis, recursive partitioning, predictive algorithms, Bayesian networks, and neural networks.

[0077] These tools and methods can be applied to several classification problems. For example, methods can be developed from comparing i) all cases vs. all controls, ii) all cases vs. non-sustained responders, iii) all cases vs. sustained responders.

[0078] In the second analytical approach, the variables selected in the cross-sectional analysis are used separately as predictors. Given the specific outcome, the random length of time each patient is observed, and the choice of proteomic and other features, a parametric approach to analyze responsiveness may be better than the widely applied semi-parametric Cox model. The Weibull parametric fit of survival allows for hazard rates to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as in the Cox model) and an accelerated failure time representation. All standard tools available for obtaining approximate maximum likelihood estimators of regression coefficients and their functions are available with this model.

[0079] In addition, Cox models can be used, particularly since reducing the number of covariates with the lasso to a manageable size greatly simplifies the analysis, allowing the possibility of a fully nonparametric approach to survival.

[0080] The analysis and database storage may be implemented in hardware or software, or a combination of both. In one embodiment of the present invention, a machine-readable storage medium is provided, the medium including a data storage material encoded with machine-readable data, which when used with a machine programmed with instructions for using said data, may display any of the data sets and data comparisons of the present invention. Such data may be used for a variety of purposes, such as patient monitoring, early diagnosis, etc. Preferably, the present invention is implemented in a computer program executed on a programmable computer, including a processor, a data storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The program code is applied to the input data to perform the functions described above and generate output information. The output information is applied to one or more output devices, in a known manner. The computer may be, for example, a personal computer, a microcomputer, or a workstation of conventional design.

[0081] Each program is preferably implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in assembly or machine language. In either case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a general-purpose or special-purpose programmable computer-readable storage medium or device (e.g., ROM or magnetic diskette) for configuring and operating a computer when the storage medium or device is read by a computer to perform the procedures described herein. The system may also be considered to be implemented as a computer-readable storage medium configured with a computer program, the storage medium so configured causing the computer to operate in a specific predefined manner to perform the functions described herein.

[0082] A variety of structural formats for the input and output means can be used to input and output information in the computer-based system of the present invention. One format of output represents test data sets having different degrees of similarity to the trusted profile. Such presentation provides one of skill in the art with a ranking of similarities and identifies the degree of similarity contained in the test patterns.

[0083] The signature patterns and their databases may be provided in a variety of media to facilitate their use. "Media" refers to a product that contains the signature pattern information of the present invention. The database of the present invention may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as magnetic disks, hard disk storage media, and magnetic tapes, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROM, and hybrids of these categories such as magnetic / optical storage media. Those skilled in the art can readily appreciate how any of the currently known computer-readable media can be used to form a product that contains a recording of the present database information. "Recorded" refers to a process for storing information on a computer-readable medium using any such method known in the art. Any convenient data storage structure can be selected based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g., word processor text files, database formats, and the like.

[0084] "Antigen" or "immunogen" refers to any substance that stimulates an immune response. The term includes killed, inactivated, attenuated, or modified live bacteria, viruses, or parasites. The term antigen also includes polynucleotides, polypeptides, recombinant proteins, synthetic peptides, protein extracts, cells (including bacterial cells), tissues, polysaccharides, or lipids, or fragments thereof, either individually or in any combination thereof. The term antigen also includes antibodies, such as anti-idiotypic antibodies or fragments thereof, and synthetic peptide mimotopes that can mimic an antigen or antigenic determinant (epitope).

[0085] A "cellular immune response" or "cell-mediated immune response" is one that is mediated by T lymphocytes or other white blood cells, or both, and includes the production of cytokines, chemokines, and similar molecules produced by activated T cells, white blood cells, or both.

[0086] "Emulsifier" refers to a substance used to make emulsions more stable.

[0087] "Emulsion" means a composition of two immiscible liquids in which small droplets of one liquid are suspended in a continuous phase of the other liquid.

[0088] An "immune response" in a subject refers to the development of a humoral immune response, a cellular immune response, or a humoral and cellular immune response to an antigen. Immune responses can generally be determined using standard immunoassays and neutralization assays known in the art.

[0089] "Immunogenic" means eliciting an immune or antigenic response. Thus, an immunogenic composition is any composition that induces an immune response.

[0090] "Pharmaceutically acceptable" refers to materials that are within the scope of sound medical judgment, suitable for use in contact with the tissues of a subject without undue toxicity, irritation, allergic response, and the like, commensurate with a reasonable benefit-to-risk ratio, and effective for their intended use.

[0091] "Reactogenicity" refers to side effects caused in a subject in response to administration of an adjuvant, immunogenic, or vaccine composition. It may occur at the site of administration and is usually evaluated in terms of the development of several symptoms. These symptoms may include inflammation, redness, and abscesses. It is also evaluated in terms of occurrence, duration, and severity. A "low" reaction is, for example, detectable only by palpitations and not by eye, or with short-lived swelling. A more severe reaction is, for example, a visible reaction, or a reaction of longer duration.

[0092] "Immunostimulatory composition" refers to a composition comprising an adjuvant as defined herein, and may optionally further comprise an antigen, in which case it may more conventionally be referred to as a vaccine. Administration of the composition to a subject results in an increase in the responsive state of bone marrow immune cells. The amount of a therapeutically effective composition may vary depending on the presence of the antigen, the adjuvant, and the condition of the subject, and can be determined by one of skill in the art. Non-antigen adjuvant compositions do not include an antigen of the disease of interest.

[0093] Adjuvant Compositions In some embodiments, an adjuvant composition is selected for use or further development. An exemplary adjuvant is an oil-in-water emulsion and may include squalene in the oil phase. For example, AS03 is an adjuvant system consisting of α-tocopherol, squalene, and polysorbate 80 in an oil-in-water emulsion. MF59 is another immunological adjuvant that includes a squalene emulsion. The dose of adjuvant administered may depend on whether an antigen is present, the antigen for which it is used, and the antigen dose applied. It also depends on the intended species and the desired formulation. Usually, the amount is within the range used for conventional adjuvants. For example, adjuvants typically include a 1 mL dose of about 1 μg to about 1000 μg, inclusive.

[0094] Adjuvant formulations may be homogenized or microfluidized. The formulation is typically subjected to a primary blending process by passing one or more times through one or more homogenizers. Any commercially available homogenizer, such as a Ross emulsifier (Hauppauge, NY), a Gaulin homogenizer (Everett, Mass.), or Microfluidics (Newton, Mass.), may be used for this purpose. In one embodiment, the formulation is homogenized for 3 minutes at 10,000 rpm. Microfluidization may be accomplished by commercially available microfluidizers, such as Model No. 110Y available from Microfluidics (Newton, Mass.), Gaulin Model 30CD (Gaulin, Inc., Everett, Mass.), and Rainnie Minilab Type 8.30H Miro Atomizer Food and Dairy, Inc., Hudson, Wis.). These microfluidizers work by forcing fluids through small openings under high pressure so that two fluid streams interact at high speeds in an interaction chamber to form a composition with submicron-sized droplets. In one embodiment, a formulation is microfluidized by passing it through a 200 micron limiting dimension chamber at 10,000 + / - 500 psi.

[0095] Routes of administration of the adjuvant composition include parenteral, oral, nasal, intranasal, intratracheal, topical, etc. The composition may be administered using any suitable device, including syringes, droppers, needleless injection devices, patches, etc. The route and device selected for use will depend on the adjuvant, antigen, and composition of interest, and such are well known to those of skill in the art.

[0096] The adjuvant composition may further include, for example, a quaternary ammonium compound (e.g., DDA), and one or more immunomodulators, such as interleukins, interferons, or other cytokines. These materials may be purchased commercially. The amount of immunomodulator suitable for use in the adjuvant composition depends on the nature and subject of the immunomodulator used. However, they are generally used in amounts of about 1 μg to about 5,000 μg per dose. In certain examples, adjuvant compositions containing DDA can be prepared by simply mixing an antigen solution with a freshly prepared solution of DDA.

[0097] The adjuvant composition may further include one or more polymers, such as, for example, DEAE dextran, polyethylene glycol, and polyacrylic and polymethacrylic acids (e.g., CARBOPOL®). Such materials can be purchased commercially. The amount of polymer suitable for use in the adjuvant composition depends on the nature of the polymer used. However, they are generally used in an amount of about 0.0001% volume to volume (v / v) to about 75% v / v. In other embodiments, they are used in an amount of about 0.001% v / v to about 50% v / v, about 0.005% v / v to about 25% v / v, about 0.01% v / v to about 10% v / v, about 0.05% v / v to about 2% v / v, and about 0.1% v / v to about 0.75% v / v. In another embodiment, they are used in an amount of about 0.02 v / v to about 0.4% v / v. DEAE-dextran can have a molecular size in the range of 50,000 Da to 5,000,000 Da, or may be in the range of 500,000 Da to 2,000,000 Da. Such materials can be purchased commercially or prepared from dextran.

[0098] The adjuvant composition may further comprise one or more Th2 stimulants, such as, for example, Bay R1005™ and aluminum. The amount of Th2 stimulant suitable for use in the adjuvant composition depends on the nature of the Th2 stimulant used. However, they are generally used in an amount of about 0.01 mg to about 10 mg per dose. In other embodiments, they are used in an amount of about 0.05 mg to about 7.5 mg per dose, about 0.1 mg to about 5 mg per dose, about 0.5 mg to about 2.5 mg per dose, and about 1 mg to about 2 mg per dose. A specific example is Bay R1005™, a glycolipid with the chemical name "N-(2-deoxy-2-L-leucylamino-β-D-glucopyranosyl)-N-octadecyl dodecane amide acetate". It is an amphiphilic molecule that forms micelles in aqueous solution.

[0099] It's a nice sight and it's a great place to stay One of them is Aceinetobacter calcoaceticus、Acetobacter paseruianus、Actinobacillus pleuropneumoniae、Aeromonas hydrophila、Alicyclobacillus acidocaldarius、Arhaeglobus fulgidus、Bacillus pumilus、Bacillus stearothermophillus、Bacillus subtilis、Bacillus thermocatenulatus, Bordetella bronchiseptica, Burkholderia cepacia, Burkholderia glumae, Campylobacter coli, Campylobacter fetus, Campylobacter jejuni, Campylobacter hyointestinalis, Chlamydia psittaci, Chlamydia trachomatis, Chlamydophila spp.. Chromobacterium viscosum pneumophilia、Moraxellsa sp.、Mycobactrium bovis、Mycoplasma hyopneumoniae、Mycoplasma mycoides subsp., mycoides LC, Clostridium perfringens, Odoribacter denticanis, Pasteurella (Mannheimia) haemolytica, Pasteurella multocida, Photorhabdus luminescens, Porphyromonas gulae, Porphyromonas gingivalis, Porphyromonas salivosa, Propionibacterium acnes, Proteus vulgaris, Pseudomonas wisconsinensis, Pseudomonas aeruginosa, Pseudomonas fluorescens C9, Pseudomonas fluorescens SIKW1, Pseudomonas fragi, Pseudomonas luteola, Pseudomonas oleovorans, Pseudomonas sp B11-1, Alcaliges eutrophus, Psychrobacter immobilis, Rickettsia prowazekii, Rickettsia rickettsia, Salmonella typhimurium, Salmonella bongori, Salmonella enterica, Salmonella dublin, Salmonella typhimurium, Salmonella choleraseuis, Salmonella newport, Serratia marcescens, Spirlina platensis, Staphlyoccocus aureus, Staphyloccoccus epidermidis, Staphylococcus hyicus、Streptomyces albus、Streptomyces cinnamoneus、Streptococcus suis、Streptomyces exfoliates、Streptomyces scabies、Sulfolobus acidocaldarius、Syechocystis sp., Vibrio cholerae, Borrelia burgdorferi, Treponema denticola, Treponema minutum, Treponema phagedenis, Treponema refringens, Treponema vincentii, Treponema palladium, and Leptospira species, including the known pathogens Leptospira canicola, Leptospira grippotyposa, Leptospira hardjo, Leptospira borgpetersenii hardjo-bovis, Leptospira borgpetersenii hardjo-prajitno, Leptospira interrogans, Leptospira icterohaemorrhagiae, Leptospira pomona, and Leptospira bratislava, and combinations thereof.

[0100] Examples of disease-causing viruses for which an immune response can be obtained include, for example, SARS-Cov1, SARS-Cov2, and other coronaviruses, avian herpesvirus, bovine herpesvirus, canine herpesvirus, equine herpesvirus, feline viral rhinotracheitis virus, Marek's disease virus, ovine herpesvirus, porcine herpesvirus, pseudorabies virus, avian paramyxovirus, bovine respiratory syncytial virus, canine distemper virus, canine parainfluenza virus, canine adenovirus, canine parvovirus, bovine parainfluenza virus 3, ovine parainfluenza virus 3, rinderpest virus, border disease virus, bovine viral diarrhea virus (BVDV), BVDV type I, BVDV Type II, Classical swine fever virus, Avian leukosis virus, Bovine immunodeficiency virus, Bovine leukemia virus, Bovine tuberculosis, Equine infectious anemia virus, Feline immunodeficiency virus, Feline leukemia virus (FeLV), Newcastle disease virus, Ovine progressive pneumonia virus, Ovine pulmonary adenocarcinoma virus, Canine coronavirus (CCV), Pantropic CCV, Canine respiratory coronavirus, Bovine coronavirus, Feline calicivirus, Feline enteric coronavirus, Feline infectious peritonitis, Virus, Porcine epidemic diarrhea virus, Porcine hemagglutinating encephalomyelitis virus, Porcine parvovirus, Porcine circovirus (PCV) type I, PCV Type II, porcine reproductive and respiratory syndrome (PRRS) virus, transmissible gastroenteritis virus, Turkish coronavirus, bovine one-day fever virus, rabies, rotovirus, vesicular stomatitis virus, lentivirus, avian influenza, rhinovirus, equine influenza virus, swine influenza virus, canine influenza virus, feline influenza virus, human influenza virus, Eastern equine encephalitis virus (EEE), Venezuelan equine encephalitis virus, West Nile virus, Western equine encephalitis virus, human immunodeficiency virus, human papillomavirus, varicella zoster virus, hepatitis B virus, rhinovirus, and measles virus, and combinations thereof.

[0101] Examples of disease-causing parasites against which an immune response can be obtained include, for example, Anaplasma, Fasciola hepatica (liver fluke), Coccidia, Eimeria spp., Neospora caninum, Toxoplasma gondii, Giardia, Dirofilaria (dog heartworm), Ancylostoma (hookworm), Trypanosoma spp., Leishmania spp., Trichomonas spp., Cryptosporidium parvum, Babesia, Schistosoma, Taenia, Strongyloides, Ascaris, Trichinella, Sarcocystis, Hammondia, and Isopsora, and combinations thereof. Also contemplated are ectoparasites, including mites, including, but not limited to, Ixodes, Rhipicephalus, Dermacentor, Amblyomma, Boophilus, Hyalomma, and Haemaphysalis species, and combinations thereof.

[0102] When added as a component of an adjuvant, the oil generally provides a long, slow release profile. In the present invention, the oil may be metabolizable or non-metabolizable. The oil may be in the form of an oil-in-water, water-in-oil, or water-in-oil-in-water emulsion.

[0103] Suitable oils for use in the present invention include alkanes, alkenes, alkynes, and their corresponding acids and alcohols, their ethers and esters, and mixtures thereof. The individual compounds of the oil are light hydrocarbon compounds, i.e., such components have 6 to 30 carbon atoms. The oils can be synthetically prepared or refined from petroleum products. The moieties can have a linear or branched structure. They can be fully saturated or have one or more double or triple bonds. Some non-metabolizable oils for use in the present invention include, for example, mineral oil, paraffin oil, and cycloparaffin.

[0104] The term "oil" is also intended to include "light mineral oil," ie, oil also obtained by distillation of petrolatum, but having a slightly lower specific gravity than white mineral oil.

[0105] Metabolizable oils include metabolizable and non-toxic oils. The oil can be any vegetable oil, fish oil, animal oil, or synthetic preparation that can be metabolized by the body of the subject to which the adjuvant is administered and is not toxic to the subject. Sources of vegetable oils include nuts, seeds, and grains.

[0106] Other components of the composition may include pharma- ceutically acceptable excipients, such as carriers, solvents, and diluents, isotonicity agents, buffers, stabilizers, preservatives, vasoconstrictors, antibacterial agents, antifungal agents, etc. Typical carriers, solvents, and diluents include water, saline, dextrose, ethanol, glycerol, oils, etc. Representative isotonicity agents include sodium chloride, dextrose, mannitol, sorbitol, lactose, etc. Useful stabilizers include gelatin, albumin, and the like.

[0107] Surfactants are used to aid in the stabilization of selected emulsions to function as adjuvants and antigen carriers. Surfactants suitable for use in the present invention include natural biologically compatible surfactants and non-natural synthetic surfactants. Biologically compatible surfactants include phospholipid compounds or mixtures of phospholipids. A preferred phospholipid is a phosphatidylcholine (lecithin), such as soybean or egg lecithin. Lecithin can be obtained as a mixture of phosphatides and triglycerides by washing crude vegetable oils with water and separating and drying the resulting hydrated gums. A refined product can be obtained by fractionating the mixture of acetone-insoluble phospholipids and glycolipids remaining after removal of triglycerides and vegetable oils by acetone washing. Alternatively, lecithin can be obtained from a variety of commercial sources. Other suitable phospholipids include phosphatidylglycerol, phosphatidylinositol, phosphatidylserine, phosphatidic acid, cardiolipin, and phosphatidylethanolamine. The phospholipids can be isolated from natural sources or can be conventionally synthesized.

[0108] Suitable non-naturally occurring synthetic surfactants for use in the present invention include sorbitan-based non-ionic surfactants, such as fatty acid substituted sorbitan surfactants, fatty acid esters of polyethoxylated sorbitol (TWEEN™), polyethylene glycol esters of fatty acids from sources such as castor oil, polyethoxylated fatty acids, polyethoxylated isooctylphenol / formaldehyde polymers, polyoxyethylene fatty acid alcohol ethers (BRIJ™), polyoxyethylene nonphenyl ether (TRITON™), polyoxyethylene isooctylphenyl ether (TRITON™ X).

[0109] As used herein, "pharmaceutically acceptable carrier" includes any and all solvents, dispersion media, coatings, adjuvants, stabilizers, diluents, preservatives, antibacterial and antifungal agents, isotonicity agents, adsorption retardants, and the like. A carrier must be "acceptable" in the sense of being compatible with the other components of the composition and not harmful to the subject. Typically, the carrier is sterile, pyrogen-free, and selected based on the mode of administration to be used. Those skilled in the art will recognize that the preferred formulation for the pharmaceutically acceptable carrier containing the composition is a pharmaceutical carrier approved by the applicable regulations promulgated by the United States (US) Department of Agriculture or the US Food and Drug Administration, or equivalent governmental agencies in countries other than the United States. Thus, a pharmaceutically acceptable carrier for commercial manufacture of the composition is a carrier that has already been approved or will be approved by the appropriate governmental agency in the United States or a foreign country.

[0110] The compositions may optionally include compatible, pharma- ceutically acceptable (i.e., sterile or non-toxic) liquid, semi-solid, or solid diluents that function as pharmaceutical vehicles, excipients, or media. Diluents may include water, saline, dextrose, ethanol, glycerol, and the like. Isotonic agents may include sodium chloride, dextrose, mannitol, sorbitol, and lactose, among others. Stabilizers include albumin, among others.

[0111] The compositions may also contain antibiotics or preservatives, including, for example, gentamicin, merthiolate, or chlorocresol. The various classes of antibiotics or preservatives to be selected are well known to those of skill in the art.

[0112] Kits may be provided. The kits may further include cells or reagents suitable for isolating and culturing cells in preparation for conversion, reagents suitable for culturing T cells, and reagents useful for determining the epigenomic effect of vaccine adjuvants. The kits may also include tubes, buffers, etc., and instructions for use.

[0113] experiment The following examples are presented to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention, nor are they intended to represent that the following experiments are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, etc.), but some experimental error and deviation should be accounted for. Unless otherwise indicated, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Celsius, and pressure is at or near atmospheric.

[0114] Example 1 Systems biology analysis of AS03-adjuvanted vaccination reveals mechanisms of adjuvantity and antibody persistence in humans. To elucidate the molecular mechanisms by which AS03 induces robust and sustained immune responses in humans, we here performed a detailed multi-omics analysis of the cellular, transcriptional, and metabolic responses to pre-pandemic H5N1 avian influenza vaccine with or without AS03 in a cohort of healthy adults. Through meta-analysis of influenza vaccine datasets, we were able to identify a significant set of genes specifically induced by AS03. Pathway analysis of these genes suggests a key role of apoptosis in the mechanism of action of AS03. Furthermore, plasma metabolomic analysis revealed that AS03-induced perturbations in lipid and fatty acid metabolism were highly associated with the expression of an AS03-specific apoptotic signature. In an independent blinded cohort of H5N1+AS03 vaccine recipients, we were able to establish an early gene signature that could successfully predict antibody response longevity. Subsequent single-cell profiling revealed RNA content differences between platelets as the main driver of this cell adhesion-associated longevity signature. Collectively, these results provide important insights into the mechanisms by which the pandemic adjuvant AS03 can induce strong and durable immunity to vaccination and may help guide the targeted development of new adjuvants to improve vaccines against future pandemics.

[0115] AS03 induces a strong early transcriptional signature that is enhanced after booster vaccination. We randomized a total of 50 healthy subjects, aged 21–45 years, in a 2:1 ratio to receive two doses of monovalent, split-virion, inactivated H5N1 clade 2.1 A / Indonesia / 05 / 2005 influenza vaccine, administered with (n=34) or without (n=16) AS03 adjuvant, 21 days apart (Figure 1A and Supplementary Table 1). To obtain a comprehensive picture of the innate and adaptive immune responses to non-adjuvanted H5N1 vaccination, as shown in Figure 1A, we performed gene expression analysis on peripheral blood mononuclear cells (PBMCs) and measured key immunological parameters of vaccine-induced B-cell and T-cell responses on the first day of vaccination and at regular intervals thereafter.

[0116] We began our investigation by examining the impact of AS03 on gene expression in PBMCs. First, we identified differentially expressed genes (DEGs) after vaccination in both adjuvanted and non-adjuvanted groups. H5N1+AS03 induced a much more robust transcriptional response than H5N1 alone, with the majority of DEGs observed at day 1 after prime and boost (Figure 1B). By examining the overlap in DEGs between both groups at different time points, we observed that the peak response in the non-adjuvanted group at day 3 after prime shared many genes with the prime and boost responses at day 1 in the adjuvanted group (Figure 8A), indicating a delayed kinetics of response in non-adjuvanted subjects.

[0117] To identify specific pathways activated in response to H5N1 vaccination, we performed gene set enrichment analysis (GSEA) on genes ranked by fold change after vaccination. As the basis for this analysis, we used a set of blood transcriptional modules (BTMs) previously identified by our group through a large-scale network integration of publicly available human blood transcriptomes. Merging BTM enrichment scores according to high-level functional categories revealed that AS03 promoted vaccination by increasing the expression of a wide range of innate and adaptive immune cells and pathways at days 1 and 7 post-prime, respectively (Figure 1C and Figure 1D). Notably, we found strong enrichment of BTMs associated with monocyte and dendritic cell (DC) activation at early time points after each immunization, whereas the response at day 7 was mainly dominated by robust B cell and plasma cell transcriptional responses. On the other hand, non-adjuvanted subjects showed very little immune activation after the prime dose and required a boost dose to achieve modest enrichment of innate responses such as antigen presentation and interferon signaling, as detailed in Figure 1E. Accordingly, B cell and plasma cell modules were only mildly modulated in the non-adjuvanted group 7 days after prime (Figure 1C), but more strongly modulated after boost, although to a lesser extent than in the adjuvanted group (Figure 1D).

[0118] In particular, while analyzing the effect of AS03 on gene expression, we noticed significant differences in transcriptional activity after each dose of the adjuvanted vaccine. Indeed, we found that several BTMs related to interferon signaling and DC activation were more strongly upregulated at day 1 after booster (d22) compared to day 1 after prime, despite an overall broad similarity in the type of immune response elicited by AS03 after each dose (Figure 1F and Figure 1G). However, gene expression analysis of responses at days 3 and 24 revealed more qualitative differences between prime and boost immunization, with multiple BTMs negatively enriched after the first dose but positively enriched after the second vaccination with AS03 (Figure 1H). Importantly, genes under the control of the transcription factor PAX3, which encode molecules with important chemotherapeutic functions for the recruitment of granulocytes, monocytes, and macrophages such as IL-8 and HBEGF, appeared to be downregulated 3 days after prime but still strongly upregulated 3 days after boost (Figure 1I and Figure S8B), thus suggesting previously unappreciated implications for the prime-boost regimen with AS03 regarding the quality, magnitude, and durability of the innate immune response to H5N1 vaccination.

[0119] Adjuvanted H5N1 vaccination promotes protective H5 head-directed antibody responses with persistence associated with a transcriptional signature of cell migration. AS03 has previously been reported to enhance antibody responses in humans in the context of influenza vaccination. Accordingly, a significant increase in H5N1A / Indonesia-specific microneutralization (MN) titers was observed at all measured time points following immunization with AS03 (Figure 2A). Booster vaccination resulted in a 70% seroconversion rate by day 42, as determined by HAI titers (Figure 8C and Supplementary Table 2). In contrast, subjects in the non-adjuvanted group demonstrated significantly lower MN and HAI responses, with no vaccine recipient achieving a four-fold increase in HAI titers over baseline levels. In addition to inducing neutralizing antibodies against the vaccine strain, AS03-adjuvanted vaccination promoted broad cross-clade neutralizing antibodies against three heterologous H5N1 strains belonging to clade 2, namely, clade 2.2.1 A / Turkey, clade 2.2.1 A / Egypt, and clade 2.3.4 A / Anhui (Figure 8D), but not against clade 1A / Vietnam strains, indicating that AS03 induces broad antibody-mediated cross-protection against phylogenetically close, but not distant, H5N1 viruses.

[0120] Furthermore, we quantified total antibody binding and polyclonal serum antibody affinity to recombinant HA1 (head) and HA2 (stem) domains derived from the boosted H5N1 vaccine strain using surface plasmon resonance (SPR) real-time kinetic assays. SPR measurements showed that subjects who received the AS03-adjuvanted H5N1 vaccine exhibited significantly higher levels of HA-binding antibodies to both the H5 head and stem subunits after both prime and boost compared to subjects in the non-adjuvanted group (Figure 8E). Importantly, while both cohorts generated antibodies targeting primarily the H5 stem domain after prime immunization, the H5 head:stem antibody ratio changed dramatically in the AS03 group after the second immunization, and a substantial increase in the concentration of polyclonal antibodies directed against the H5 head domain continued over time (Figure 2B) and correlated with MN titers (Figure 8F). In contrast, study participants who received H5N1 alone developed only a small increase in the concentration of H5 head-specific antibodies after vaccination, and the antibody response in this vaccine group was mainly limited to a moderate increase in H5 stem-specific antibody titers. Furthermore, we revealed that while the dissociation rate of the antigen-antibody complex was comparable to the non-adjuvanted group after prime, antibody affinity to the H5 head domain was significantly higher in the AS03 group after boost (Figure 2C) and correlated with MN titers (Figure 8G). Predictably, H5N1 alone was able to induce only limited antibody affinity maturation against the H5 head and stem domains (Figure 2C), thus suggesting relapse of memory recall responses rather than epitope spreading of the antibody repertoire.

[0121] The recent COVID-19 crisis reinforced the importance of developing pandemic vaccines that could induce long-term protection, especially in scenarios where multiple epidemic waves may occur and vaccine demand may exceed supply, such as in the early stages of a pandemic. Here, we observed a four-fold decrease in geometric mean MN and HAI titers between days 42 and 100 after prime H5N1+AS03 vaccination (Figures 2A and 8C). Comparison of antibody responses to H5N1+AS03 with those to TIV showed that antibody titers to avian influenza showed a similar magnitude of decrease to those to seasonal influenza strains within a few months after vaccination (Figure 9A). As a measure of relative persistence of antibody responses, we calculated residuals from a linear fit between day 42 HAI titers and day 100 HAI titers (Figure 9B). This approach removes the dependency of day 100 titers on the initial day 42 response, and residuals can be viewed as "normalized" day 100 titers, with positive values ​​indicating subjects with more sustained responses than the mean.

[0122] First, we determined whether the day 7 "plasmablast signature" observed by us and others in previous studies with seasonal influenza vaccination that was shown to correlate with day 28 HAI titers correlated with persistence of antibody responses. As previously seen in our studies of seasonal influenza vaccines, no correlation was observed with persistence (Figures 2C and 2D).

[0123] To identify transcriptional signatures associated with sustained antibody responses, we performed GSEA on genes ranked by correlation with day 100 / day 42 residuals (Figure 2D). Enriched signatures appeared to differ between doses, with expression of cell cycle-related modules at day 7 post-prime and cell adhesion / platelet activation-related modules at days 1-7 post-boost associated with increased persistence. We compared these results to previously identified signatures of persistence in TIV vaccination and found significant overlap (Figure 2D). Specifically, genes within the platelet activation / actin binding module M196 showed strong concordance in correlation with antibody persistence in both studies (Figure 2E and Figure 2F), indicating a common mechanism in the development of long-lived antibody responses to influenza vaccination.

[0124] To further validate the robustness of these signatures of antibody persistence, we used a machine learning approach to train a classifier that could predict antibody persistence in an independent, blinded test set. Briefly, we trained a linear regression model based on BTM-level features to predict day 100 / 42 HAI residuals using the H5N1+AS03 data as a training set and the 2010 / 2011 TIV data as a validation set (see Methods for details). Using day 28 / 21 gene expression data, we were able to build a model in which the predicted day 100 / 42 HAI residuals significantly correlated with the residuals measured in the blinded test set (Figure 2G). Notably, analysis of genes within these predictive modules showed that many of those with the highest association with antibody persistence were involved in cell adhesion / migration (CTTN, CDHR5, MYLK) and / or highly expressed in platelets (SELP, PROS1, PF4, PPBP) (Figure 2H), indicating a potential role for cell migration and platelets in establishing sustained antibody responses to AS03-adjuvanted vaccination.

[0125] CITEseq analysis reveals a platelet origin for the transcriptional signature of antibody persistence. Prompted by our findings, we sought to investigate the cellular origin of this newly identified transcriptional signature of antibody persistence. To achieve this, we performed CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) to construct single-cell protein and transcriptome landscapes of PBMCs at days 21 and 28 from three "persistent" and three "attenuated" antibody responders to H5N1+AS03 vaccination (HAI residuals >0 and <0 at day 100 / 42, respectively). After an initial preprocessing step, we obtained transcriptomes for 62,789 cells. Through dimensionality reduction via UMAP and graph-based clustering (see Methods for details), we were able to identify 26 distinct cell clusters that were evenly distributed across all samples and time points (Figure 10A). Following cell type annotation and cell-by-cell quality control processing to exclude low quality cells (see Methods section and Supplementary Figures 4A-C), we constructed single immune cell landscapes (Figure 3A) and found that antibody lifespan-related genes in the predicted signature (as reported in Figure 2H) were indeed highly expressed in platelets, as previously suggested by bulk PBMC gene expression analysis, as well as in a small cluster of cells sharing features of both monocytes and platelets (Figure 3B).

[0126] We next set out to investigate how the differences in the predictive gene signatures between continuing and attenuating responders emerged at the single-cell level. To confirm that the CITE-seq analysis captured the same immune response dynamics as the microarray data, we compared the day 28 / 21 FC of DEGs measured by microarray to the pseudo-bulk estimates from CITE-seq (Figure 10B). In this comparison, five subjects showed strong correlation between the two measurements, while one subject showed no concordance and was therefore excluded from downstream analysis. Overall, in accordance with the bulk PBMC microarray results, continuing responders had significantly higher pseudo-bulk expression of the antibody persistence signature compared to attenuating responders (Figure 3C, left panel). Importantly, by excluding one cell cluster at a time from the pseudo-bulk calculation, we found that excluding the platelet cluster from the analysis resulted in a near-complete loss of the transcriptional differences observed between continuing and attenuating antibody responders (Figure 3C, right panel). This "exclusion approach" again demonstrated that the persistent signatures observed in bulk PMBC analysis were mostly derived from platelets.

[0127] However, no clear differences in platelet frequency were found between sustained and attenuated responders (Figure 3D). Additionally, when comparing normalized expression per cell within the platelet population, no significant differences were observed in the day 28 / 21 gene signatures between the two groups (Figure 10C). In contrast, we documented a significant decrease in total reads within the platelet population between days 21 and 28, which did not occur specifically among sustained responders (Figure 3E), thus indicating a decrease in RNA content within platelets, rather than platelet frequency, as the primary cause of the transcriptional differences captured in the antibody persistence signature between sustained and attenuated responders.

[0128] In addition to identifying the cellular origin of the predicted antibody persistence signatures, we also asked whether we could identify additional cell-specific differences between continuing and attenuated responders that were not initially detected by bulk expression measurements. To this end, we performed an unbiased comparison and determined the DEGs between cells from continuing and attenuated responders at each time point and within each cluster, followed by an over-representation analysis of genes included in the BTMs. Of particular interest were the differences between plasmablasts in spliceosome and electron transport expression (Figure 3F). Comparing genes within these modules, we observed that plasmablasts from continuing responders had elevated expression of genes encoding small nuclear ribonucleoproteins and mitochondrial respiratory electron transport complexes compared to attenuated responders (Figure 3G). These results are consistent with recent findings indicating oxidative phosphorylation and mitochondrial remodeling as key metabolic processes required for B cell activation and antibody production by plasma cells.

[0129] The frequency of vaccine-induced T follicular helper cells in blood correlates with neutralizing antibody titers and antibody avidity. The generation of neutralizing antibodies with high affinity to H5 antigens promoted by AS03 prompted us to investigate the role of T follicular helper (Tfh) cells in the context of H5N1 vaccination. Tfh cells are important for affinity maturation of B cells in germinal centers (GCs) and have been previously monitored after immunization with AS03 in animal models. Although bornified GC Tfh cells are not commonly detected in peripheral blood, we measured a population of circulating Tfh-like CXCR5+CD4+ T cells, previously described as functionally similar to GC Tfh cells, before and after H5N1 vaccination. We found that immunization with H5N1+AS03 resulted in an increase in the frequency of activated, PD-1+ICOS+, blood Tfh cells at day 7 after each immunization (Figure 4A). Strikingly, the increase in activated Tfh observed after boosting was consistent and correlated with increases in both MN titers (Figure 4B) and antibody affinity to the H5 head domain (Figure 4C). Conversely, we did not detect any significant changes in activated Tfh cell frequencies after vaccination in the non-adjuvanted group.

[0130] To further characterize vaccine-induced blood Tfh cells, we FACS-sorted PD-1+ICOS+CXCR5+CD4+activated and PD-1-ICOS-resting CXCR5+CD4+ T cells on days 7 and 28 and profiled their gene expression using Clariom S technology. Additionally, since a previous study described the ability of CXCR3 and CCR6 markers to distinguish peripheral inefficient Tfh1 (CXCR3+CCR6-) cells and efficient Tfh2 (CXCR3-CCR6-) or Tfh17 (CXCR3-CCR6+) cells based on their B cell helper potential (Schmitt et al., 2014), we sought to explore whether Tfh cell polarization could explain the differences in activated Tfh frequencies observed between the two vaccine groups after immunization.

[0131] Gene expression analysis of sorted Tfh subsets (as presented in Fig. S12A) revealed significant transcriptional differences between activated and resting Tfh cells that were consistent in both Tfh1 and Tfh2 populations (Fig. S4D). Activated Tfh upregulated the expression of the FGL2 gene, which encodes fibrinogen-like protein 2, a key immune regulator of both innate and adaptive responses constitutively secreted by both CD4+ and CD8+ T cells (Marazzi et al., 1998), and also FGFBP2 (formerly known as KSP37), whose product is selectively produced by lymphocytes with cytotoxic potential. Strikingly, we also noticed a strong regulation of genes encoding proteins with known antimicrobial properties typical of innate immune cells, such as lysozyme (LYZ) and IL1β, in addition to the C-type lectin receptor CLEC7A, typically found in myeloid cells, and the S100 calcium-binding proteins S100A8, S100A9, and S100A12, abundant in monocytes. Accordingly, GSEA analysis of DEGs revealed a significant enrichment of monocyte-associated BTMs and suggested the possible infiltration of other cell types besides blood Tfh in our sorted populations (Figure 12B). These results were clearly unexpected, since our aim was the selective isolation of peripheral CXCR5+CD4+ T cell subsets in different activation states. To further investigate these findings, we used CIBERSORT, a computational method developed to deconvolute cell type proportions using cell type-specific gene expression references. In agreement with our GSEA analysis, the CIBERSORT algorithm identified a strong enrichment of monocyte-specific genes in the activated Tfh population (Figure 12C), thus estimating a significant and selective presence of monocytes among the sorted activated but not resting blood Tfh cells (Figure 4E). A recent study described for the first time the presence of CD4+ memory T cell-monocyte complexes in human blood. Notably, these CD3+CD14+ T cell-monocyte duplexes form during active immune responses, adding a live singlet gate when observed using conventional flow cytometry. The origin and biological role of these cell associations in the peripheral circulation are currently unknown.

[0132] Given our initial interest in transcriptional mechanisms related to Tfh activation, we used CIBERSORTx to estimate CD4 T cell-specific expression in sorted Tfh cells (thus computationally excluding the monocyte component) and then performed GSEA to identify BTMs enriched in activated versus quiescent Tfh. Not surprisingly, we found strong enrichment in cell cycle and energy metabolism-related transcriptional modules, indicative of a higher metabolic state and increased proliferation for activated Tfh cells compared to their quiescent counterparts (Figure 4F and Figure 4G). Our analysis also suggests that the activation process of Tfh cells may revolve around polo-like kinase 1 (PLK1), one of the key kinases controlling T cell survival, differentiation, and expansion (Figure 4F and Figure 4H). Finally, gene-level analysis of sorted Tfh cell populations, followed by unsupervised clustering, successfully separated Tfh subsets based on their activation status, highlighting a strong upregulation of genes encoding histone proteins and interferon-stimulated genes (such as ISG15, known to enhance IFN-γ production) in activated Tfh cells (Figure 4I). No major gene expression clusters were observed that could distinguish between activated Tfh types (Tfh1 vs. Tfh2), or Tfh activated at different time points (d7 vs. d28). Importantly, AS03+H5N1 induced a significantly higher frequency of circulating activated Tfh after vaccination compared to H5N1 alone, although no major differences at the transcriptional level were found between activated Tfh cells isolated from subjects of the two vaccinated groups. Taken together, these data suggest that AS03-adjuvanted H5N1 vaccination promotes a higher frequency of circulating activated Tfh cells after both prime and boost vaccination, compared to non-adjuvanted vaccination. The frequency of activated Tfh cells positively correlated with neutralizing antibodies and antibody affinity to the H5 head domain in the adjuvant groups, thus indicating an AS03-driven T cell-dependent mechanism of B cell affinity maturation and antibody production.

[0133] Early molecular signatures are associated with multiple markers of post-boost immune responses. The strong induction of antibody and Tfh cell responses measured after vaccination in the adjuvant group led us to ask whether it was possible to identify an early transcriptional signature common to multiple adaptive immune parameters induced by AS03, specifically: i) activated Tfh frequency, ii) H5 head-directed antibody affinity maturation, and iii) increased neutralization titers (Figure 5A). Importantly, these three measurements alone were able to efficiently separate subjects by vaccine group, as shown in Figure 5B, thus implying substantial molecular and cellular differences in the biological mechanisms leading to the generation of adaptive immunity. GSEA performed on genes ranked by correlation with all three parameters revealed that the highest number of generally enriched modules occurred on day 1 post-prime (Figure 5C), suggesting that a strong primary response is required for the robust adaptive response induced by AS03 to the boost dose. Many of the modules commonly enriched on day 1 post-prime were related to inflammatory and interferon signaling, as well as monocyte and DC activation (Figure 5D). These results are consistent with data from a previous systems-based study with AS03 in a clinical setting, where interferon-regulated and monocyte activation-related genes were found to be significantly upregulated within 24 hours after AS03-adjuvanted H1N1 and H5N1 influenza vaccination. Interestingly, the strong downregulation in several T and B cell modules on day 1 also correlated with the three highly correlated immune parameters under investigation.

[0134] At the gene level, we found that robust day 1 upregulation of NLRP3 inflammasome genes was strongly associated with both B and T cell responses induced by adjuvant vaccination with AS03 (Figure 5E). Alum and squalene-based emulsions have previously been shown to induce NLRP3 inflammasome activation, but the requirement of NRLP3 for these adjuvants to promote adaptive immunity remains debated. Additionally, we noticed that overexpression of interferon-related genes IRF8 and ATF3 was also positively associated with the strong adaptive immune features promoted by AS03 (Figure 5E). IRF8 is a transcriptional and interferon regulator highly expressed in myeloid cells whose function is essential for the development of monocytes and dendritic cells from common progenitor cells. In myeloid cells, IRF8 regulates the expression of Bax and Fas to regulate apoptosis. Interestingly, two recent papers have reported a critical role for IRF8 in the activation of both NRLC4 and NRLP3 inflammasomes to neutralize bacterial infection in vivo. On the other hand, ATF3 is induced by a wide variety of physiological stresses and integrates diverse signals arising from inflammatory events, metabolic stress responses, and apoptotic processes. SCO2, an electron transport chain gene that encodes a metabolic regulator that is important for the generation of ATP and has a role in preventing hypoxia-induced cell death, was also highly upregulated after vaccination with AS03 and positively associated with B and T cell features in the analysis. Interestingly, the squalene-based adjuvant MF59, an AS03 analog, has been shown to depend on initial ATP production and extracellular release for its mechanism of adjuvantity. Currently, the potential role of SCO2 in this process is unclear. Furthermore, expression of SORT1, which encodes a protein transporter of the trans-Golgi network that regulates lipid metabolism while also functioning as a multiligand receptor for inflammatory cytokines including IFN-g and IL-6 in immune cells, was also paralleled by the increase in the three AS03-induced adaptive immune parameters.Finally, downregulation of TRAF1 on day 1 was negatively associated with increased Tfh activation, antibody-mediated neutralization, and affinity maturation. This gene product functions as a negative regulator of inflammation and mediates anti-apoptotic signals from TNF receptors in cooperation with TRAF2 and IAPs. Its suppression provides further evidence that the early formation of a pro-inflammatory, pro-apoptotic environment after AS03 injection favors both B and T cell adaptive immune responses to vaccination.

[0135] Meta-analysis of influenza vaccine trials reveals AS03-specific transcriptional signatures that correlate with activated Tfh frequencies in the periphery. While the direct comparison between AS03-adjuvanted and non-adjuvanted H5N1 vaccination in this study provided valuable insights into the cellular and molecular mechanisms of AS03 adjuvantity, we sought to extend our study to additional influenza datasets available in the literature to further analyze the unique role of AS03 adjuvant in modulating the immune response to vaccination. With this in mind, the key question we wanted to address was to what extent does the immune response to H5N1+AS03 have in common with responses to seasonal influenza strains. To this end, we used data from our previous study examining responses to trivalent inactivated influenza vaccines (TIVs) to perform GSEA on genes ranked by fold change post-vaccination on days 1–7 across multiple influenza seasons. There was a high degree of overlap between enriched pathways in response to both vaccines, particularly on days 1 and 7 (Figure 13A). These results differ from and build on previous findings in this study, where much larger transcriptional differences in both magnitude and kinetics were observed when comparing responses between non-adjuvanted and AS03-adjuvanted H5N1 vaccines. Our data reflect the ability of AS03 to rapidly elevate the immune system activation state to levels comparable to those observed after seasonal TIV vaccination, while boosting naive immune responses to avian-derived influenza antigens to which humans have no pre-existing immunity.

[0136] Although the responses to H5N1+AS03 and TIV were very similar at a broad level, we wondered whether it was possible to identify transcriptional signatures unique to AS03, and therefore not normally present after immunization with seasonal TIV or non-adjuvanted H5N1, that may reflect specific mechanisms by which adjuvants induce strong immune responses. To achieve this goal, we incorporated data from both the prime and boost doses of this study, as well as publicly available data from previous studies of responses to AS03-adjuvanted H1N1 vaccination, and compared this to gene expression data from multiple TIV studies (Figure 6A; see also Supplementary Methods). GSEA of genes ranked by the mean t-statistic between AS03 and seasonal responses revealed multiple neutrophil-related modules, as well as a WNT / retinoic acid receptor (RAR) signaling module (Figure 13B), which showed significant enrichment in the AS03-adjuvanted compared to seasonal responses (Figure 13C).

[0137] We also extended this approach to the gene level by pairwise identifying a common set of genes that were differentially expressed in all AS03 datasets compared to each seasonal dataset (Figure 6A). We obtained a list of 11 genes (Figure 6B), many of which are known to be involved in WNT / β-catenin signaling, such as ANKRD22, KREMEN1, TGM2, KLF4, TMEM159, and STRN, suggesting a possible role for this pathway in the mechanism of action of AS03. Importantly, to our knowledge, none of these 11 genes or their products have yet been linked to the mechanism of action of AS03 or described to contribute to the generation of immunity against influenza more generally.

[0138] To further explore the status of these genes during response to AS03 adjuvant vaccination, the set of significantly correlated partner genes in all AS03 datasets was determined for each AS03-specific DEG. These partner sets revealed that three of the DEGs, ANKRD22, KREMEN1, and TGM2, were highly correlated with each other and shared a large number of co-correlated genes (Figure 13D). KREMEN1 is known to form a complex with Dickkopf1 (DKK1) and LDL receptor-related protein 6 (LRP6) to negatively regulate WNT signaling, but this gene has been further described to also function as a dependence receptor that mediates programmed cell death in a WNT-independent manner by inducing caspase 3 activation. Although little is known about the function of ANKRD22 in immunity, it has been reported that this gene is highly expressed in several cancer tissues and its product promotes cancer progression by significantly acting in metabolic reprogramming of cancer cells. In contrast, the role of TGM2 in the immune system is better characterized. This gene encodes a multifunctional enzyme belonging to transglutaminases that has both pro- and anti-apoptotic roles. In DCs, TGM2 mediates the maturation of antigen-presenting cells in response to bacterial LPS, and its inhibition significantly reduces cytokine production and DC differentiation. TGM2 activity also plays an important role in monocyte differentiation into macrophages and DCs. Consistent with this, deconvolution analysis in our study showed that the strong upregulation of KREMEN1, ANKRD22, and TGM2 1 day after prime and boost vaccination with AS03 could be traced back to changes in myeloid cell gene expression, especially DCs and monocytes, but not lymphocytes (Figure 13E).

[0139] To confirm the identification of a novel gene signature that could be used as a robust marker of early response to AS03, we used an artificial neural network-based machine learning classification algorithm trained on expression data of three "core" AS03-specific genes identified from our study, KREMEN1, ANKRD22, and TGM2, to predict vaccine status (adjuvanted vs. non-adjuvanted) in three blinded test sets containing expression data from an independent clinical study of response to H5N1 vaccination with or without AS03 (see Supplementary Methods). The classifier achieved excellent predictive accuracy (>90%) when tested on gene expression data from sorted monocytes and total PBMCs, thus validating the reproducibility of this AS03-specific signature in external testing (Figures 6C and 6D).

[0140] Notably, expression of KREMEN1, TGM2, and ANKRD22 at day 1 post-boost was positively correlated with activated Tfh frequency at day 7 post-boost (Figure 6E), which may suggest an important role of these genes in regulating AS03-induced T cell responses. Interestingly, expression of these three genes was not significantly associated with MN titers (data not shown). These results may indicate that AS03 may have led to the generation of potent T and B cell responses through multiple non-overlapping mechanisms. Accordingly, expression of only one gene in our AS03-specific signature, TMEM159, strongly correlated with MN titers (but not with activated Tfh frequency) in both our and other publicly available datasets, where AS03 was co-administered with influenza antigen (Figure 6F and Figure 6G). Surprisingly, the biological role of the gene product of TMEM159 remained elusive until recently, but an elegant study by Chung et al. revealed that this protein, now renamed lipid droplet assembly factor 1 (LDAF1), forms a complex with seipin (encoded by the BSCL2 gene) to determine the site and catalyze lipid droplet formation in the endoplasmic reticulum (ER). In light of these findings, the AS03-induced downregulation of TMEM159 within the first 24 h after vaccination may indicate an inhibition of de novo lipid droplet formation, possibly as a consequence of the accumulation of squalene, the natural precursor of cholesterol, of which the AS03 adjuvant is mainly composed, and neutral lipids in the cytosol of immune cells. Although these mechanisms require further investigation, our results seem to suggest that the early AS03-induced biological phenomenon underlying the downregulation of TMEM159 may represent a determinant for the generation of the strong neutralizing antibody response observed several weeks later (Figure 6F and Figure 6G).

[0141] AS03 enhances immunogenicity through the regulation of immune cell metabolic pathways. The involvement of some of the AS03 "core" genes, including KREMEN1 and TGM2, in biological events related to apoptosis prompted us to explore the potential role of programmed cell death in the mechanism of action of AS03-adjuvanted vaccines. In context, while analyzing the transcriptome of early signatures associated with multiple measures of B and T cell immunogenicity, we identified several genes known to be involved in the regulation of cell death and survival, such as IRF8, SCO2, and TRAF1, which may indicate apoptotic signals as key factors in the generation of AS03-driven adaptive immunity (Figure 5).

[0142] To further explore these mechanisms, we searched the Reactome database for a canonical list of genes involved in different stages of apoptosis and pairwise searched for DEGs in the AS03 dataset compared to both non-adjuvanted H5N1 or seasonal TIV vaccination. Notably, we found significant changes in the expression of 40 apoptotic genes, many of which were more strongly regulated at day 1 post-boost (day 22) and specifically induced after vaccination with AS03, but not after administration of a non-adjuvanted vaccine, arguing for an intrinsic role of the adjuvant in triggering mechanisms related to programmed cell death (Figures 7A and 7B). Interestingly, we observed modulation of genes known to be involved in both the intrinsic and extrinsic pathways of apoptosis, suggesting that multiple stimuli from both inside and outside the cell may contribute to this process. Transcriptional changes in the extrinsic apoptosis pathway appeared to involve the death receptor TRAIL (TNFSF10) and TLR4 / MD-2 (LY96) axis, as indicated by the strong upregulation of these genes, whereas intrinsic mechanisms of apoptosis under mitochondrial control involved downregulation of genes encoding the antiapoptotic molecules BCL-2, AKT2, and AKT3, and upregulation of the proapoptotic molecules BID and the caspase activator cytochrome C (CYCS) (Figure 7B). CASP7, encoding executioner caspase 7, was also significantly more upregulated in the AS03+H5N1 group at day 1 after both prime and boost, compared to the non-adjuvanted dataset. Consistent with these findings, we recently reported that immunization with the squalene-based oil-in-water emulsion vaccine adjuvant MF59 induces cell death-related signaling in macrophages present in lymph nodes following MF59 uptake in a mouse model vaccination.Moreover, we found here that AS03 vaccination promoted a much stronger upregulation of genes encoding proteasome proteins (PSM-genes), including all three catalytic subunits typical of the immune proteasome, PSMB8, PSMB9, and PSMB10 (also known as LMP7, LMP2, and MECL1, respectively), suggesting an adjuvant-driven engagement of a highly efficient protein degradation mechanism with distinct immune properties and an established role in managing oxidative stress.

[0143] Previous literature has identified biomarkers of apoptosis as positive predictors of influenza vaccine responsiveness in humans. In light of our findings, we sought to investigate cellular events associated with programmed cell death signaling occurring after immunization with AS03 that may contribute to immunogenicity. Given the involvement of several AS03 "core" genes, such as ANKRD22, TMEM159, KLF4, and TGM2, in controlling key metabolic processes, especially lipid accumulation and metabolism, we hypothesized that AS03 may promote important changes in immune cell metabolism and therefore asked whether perturbations in the blood metabolome could be detected through untargeted high-resolution metabolomics. Indeed, principal component analysis (PCA) on fold-change values ​​of differentially abundant metabolite peaks obtained using mummichog software revealed a bifurcation of metabolic trajectories following AS03-adjuvanted vs. non-adjuvanted H5N1 vaccination, highlighting substantial metabolic differences between vaccine groups, especially within the first 24 h after each immunization (Figure 7C and Figure 7D). Differential feature enrichment analysis revealed AS03-induced changes in multiple pathways related to lipid metabolism and fatty acid metabolism and activation, as shown in Figure 7E. Importantly, we noticed a significant correlation between gene expression changes in apoptosis pathways and the abundance of certain metabolites (Figure 7F). Of particular interest, we found a strong association between apoptosis-related genes and metabolic perturbations in fatty acid and carnitine shuttle pathways. The carnitine shuttle represents a system in which long-chain fatty acids, which are impermeable to the mitochondrial membrane, are transported into the mitochondrial matrix to undergo β-oxidation and generate energy in the form of acetyl-CoA (which then enters the citric acid cycle). Consistently, significant correlations between other metabolic pathways related to β-oxidation of saturated fatty acids, including peroxisomal oxidation, and the apoptosis gene signature were also identified, suggesting that AS03 may induce rapid and strong changes in cellular fatty acid metabolism within the first 24 hours after vaccination.Since fatty acid oxidation is known to be one of the main sources of reactive oxygen species (ROS) generation, we hypothesize that excessive ROS production as a result of increased fatty acid oxidation may represent one of the main signals by which AS03 adjuvant induces the immunogenic mechanism of programmed cell death. Consistent with this hypothesis, AS03-induced metabolic changes in fatty acid activation and metabolism were strongly associated with the generation of vaccine-specific neutralizing antibodies (Figure 7F).

[0144] We present here a detailed multi-omics analysis of the cellular, transcriptional, and metabolic responses to a pre-pandemic H5N1 avian influenza vaccine administered with and without the squalene-based emulsion adjuvant AS03 in a cohort of healthy volunteers. By extending our analysis to several other AS03-adjuvanted and non-adjuvanted influenza study datasets deposited in public repositories, we were able to significantly expand previous systems biology reports on the use of AS03 in humans and identified several gene signatures, metabolic networks, and biological processes not previously evaluated, whose unique regulation within the first few days after vaccination with AS03 adjuvant may be relevant, and in some cases predictive, to one or more assessment criteria of vaccine immunogenicity in this and independent clinical studies.

[0145] Among the first findings in this study are both quantitative and qualitative transcriptional differences observed in the innate immune response after prime and boost immunization with AS03. As an example, this is the case for genes regulated by the transcription factor PAX3, encoding an important chemoattractant and regulator of innate immune cells, whose upregulation was more pronounced and longer-lasting after boost compared to the same time point after prime immunization. The relatively new concept of "innate immune memory" (or "trained immunity"), the phenomenon that innate immune cells such as monocytes, macrophages, or NK cells can temporarily "remember" previous exposure to endogenous or exogenous stimuli through epigenetic modifications, thus altering their behavior upon subsequent immunization, has only been explored to a small extent in the context of AS03-adjuvanted vaccination strategies. However, previous clinical studies have shown how similar mechanisms may be applied to other adjuvants such as AS01 and AS02, as well as antigens. In this context, with several COVID-19 vaccine technology platforms in late stages of development incorporating adjuvants and requiring multiple immunizations to induce and maintain protection over time, there is an unprecedented opportunity to systematically explore and define the impact of trained immunity-related phenomena on vaccination outcomes on a global scale. These studies could be of strategic importance to inform clinical practice and identify optimal homologous or heterologous prime-boost vaccination regimens, thus enabling more efficient global vaccination campaigns.

[0146] While the immune response to natural infection with influenza virus in humans is relatively broad and long-lived, vaccine-induced immunity primarily induces a systemic antibody response that tends to decline over time. The primary objective of our study was the investigation of the molecular mechanisms underlying vaccine-induced persistent antibody responses. Here we identified a blood transcriptional signature of cellular trafficking associated with a more sustained antibody response to AS03-adjuvanted H5N1 vaccination, which we later used to successfully predict antibody persistence in a blinded manner in an independent clinical study using the same vaccine. Notably, CITE-seq experiments identified platelets as the cellular origin of this longevity signature, thus indicating that these cells potentially play a role in the formation of long-lived antibody responses to vaccination. Previous studies in mice have demonstrated that bone marrow-resident megakaryocytes, which are platelet precursors, constitute a functional component of a microenvironmental niche that is important for the generation and maintenance of long-lived plasma cells by interacting with and producing the plasma cell survival factors APRIL and IL-6. Whether a similar mechanism could be involved in the generation of long-lived antibody responses to vaccination in humans remains unclear at present. Furthermore, CITE-seq analysis revealed that attenuated antibody responders exhibited a much more rapid decline in platelet RNA content after the second vaccination compared to more sustained responders. Although platelets lack the ability to synthesize genomic DNA and new mRNA, they inherit mRNA and ribosomes from progenitor bone marrow-resident megakaryocytes when newly released in the peripheral circulation. Thus, the overall decline in platelet RNA content observed in attenuated antibody responders may reflect a process of cellular maturation and senescence, with progressive loss of early megakaryocyte characteristics in favor of a more mature platelet phenotype. However, platelets also have the ability to horizontally transfer RNA to other cells, such as monocytes and endothelial cells, and subsequently alter the expression profile of recipient cells to regulate inflammation and vascular homeostasis. Further studies are needed to clarify the mechanisms by which platelets and megakaryocytes contribute to long-lasting antibody responses in humans.

[0147] Finally, by performing a meta-analysis of AS03-adjuvanted and non-adjuvanted influenza vaccine datasets, we were able to identify a common set of genes specifically induced by AS03 that had not previously been associated with the mechanism of action of adjuvants or known to contribute to the generation of immunity to influenza. By using day 1 changes in expression of three of these genes, TGM2, ANKRD22, and KREMEN1, we were able to predict the use of adjuvants in external trials with over 90% accuracy. Notably, early transcriptional changes in AS03 "core" genes were strongly associated with the frequency of activated Tfh cells in the periphery 7 days after vaccination, suggesting that these genes may be involved in mechanisms of immunogenicity. Contextually, pathway analysis supported a key role for the intrinsic (mitochondrial) and extrinsic pathways of apoptosis in the mode of action of AS03. Consistent with this, we previously found that immunization with the squalene-based emulsion adjuvant MF59 induced apoptotic signals in macrophages present in lymph nodes following adjuvant uptake in mice. Importantly, in vivo coadministration of pan-caspase inhibitors and MF59 significantly suppressed the production of adjuvant-enhanced IgG antibody responses, highlighting the critical role of apoptosis and caspases in the mechanism of action of squalene emulsion adjuvants. Endogenous stresses such as DNA damage, hypoxia, overproduction of ROS, or metabolic dysfunction have all been established as potential causes of mitochondrial apoptosis. Accordingly, plasma metabolomics analysis revealed that AS03-induced early perturbations in lipid and fatty acid oxidation and metabolism were highly correlated with the expression of AS03-induced genes involved in mitochondrial apoptosis, as well as with neutralizing antibody titers several weeks later. Of note, we have already found that fatty acid metabolism is a key orchestrator of antibody responses to influenza vaccines.Overall, our results are consistent with previous studies in mice with AS03, where a gene signature of altered lipid metabolism could be detected in the draining LNs of immunized mice within 2 hours after vaccination.Similarly, it has previously been shown that in vitro uptake of other squalene-containing emulsion adjuvants by phagocytic and non-phagocytic cells results in lipid alterations and accumulation of neutral lipids in the form of cytoplasmic lipid droplets.

[0148] In conclusion, our findings reveal previously unappreciated biological mechanisms associated with AS03 adjuvantity and antibody persistence after pre-pandemic H5N1 vaccination in humans, thus highlighting again the enormous potential of systems biology approaches to accelerate vaccine research and development.

[0149] Example 2 Use of platelets to predict durability of immune responses - Patents.com result Platelet RNA content positively correlates with the duration of antibody responses to vaccination. To validate our CITE-seq findings (Figure 3), we used flow cytometry analysis to assess RNA content in platelets from subjects immunized with TIV during the 2010-11 influenza season (Figure 14A). FSC / SSC dot plots of thawed PBMC samples showed that the CD41 + CD61 + These samples show a platelet population, characterized by low SSC and FSC values, that can be easily distinguished from PBMCs by size (Figure 15A). The frequency of platelets in these samples was comparable to total PBMCs and did not differ between days 0 and 7 after TIV vaccination (Figure 15B), supporting the idea that platelet signatures arise due to intrinsic differences within platelets, rather than different numbers of platelets. Importantly, RNA content and %RNA in total platelets at day 7 vs. day 0 in vaccinees were significantly higher than that in vaccinees at day 0. +Platelet fold change (Figure 15C) positively correlated with day 180 / day 42 residuals (Figure 14B), confirming the association between platelet RNA content and the persistence of antibody responses to vaccination.

[0150] To explore the mechanisms underlying this association in more detail, we next examined the response to AS03-adjuvanted SARS-CoV-2 subunit Spike protein in rhesus macaques (Figure 14C). Similar to human samples, FSC / SSC dot plots of NHP samples also showed significant platelet counts (Figure 15D). Neutralizing antibody responses peaked on day 42 and then gradually declined (Figure 14D). RNA content and %RNA in total platelets at day 7 versus baseline + Platelet fold change correlated with day 180 / day 42 residuals (Figure 14E and Figure 15E). Consistently, a positive correlation was observed between day 7 platelet RNA content fold change and week 42 / week 20 residuals in NHP subjects vaccinated with R848-adjuvanted HIV subunit gp140 (Figure 14F-H, Figure 15F, and Figure 1G). As long-term antibody titers are mainly generated by bone marrow-resident long-lived plasma cells (LLPCs), the frequency of myeloid antigen-specific plasma cells may be an indicator of sustained neutralizing antibody responses (Kasturi et al., 2015, Science Immunology). Our data showed that day 7 platelet RNA content fold change was also associated with the number of bone marrow ASCs at week 48 (Figure 14I). To corroborate our findings in humans and NHPs, we assessed platelet RNA content using mice vaccinated with AS03-adjuvanted SARS-CoV2 subunit Spike (Figures S14J, S14K, S15H, and S15I) and found that the fold change in platelet RNA content at day 7 correlated with post-boost day 42 / day 7 remaining or number of ASCs at day 42 (Figures S14L, S14K).

[0151] Previous studies in mice have demonstrated that bone marrow-resident megakaryocytes, which are platelet precursors, constitute a functional component of a microenvironmental niche that is important for the generation and maintenance of long-lived plasma cells (LLPCs) by interacting with and producing the plasma cell survival factors APRIL and IL-6 (Winter et al., 2010, Blood). Our results demonstrate that peripheral platelet RNA content can reflect the status of megakaryocytes and other processes in the bone marrow that contribute to LLPC survival and point to platelet RNA content as a biomarker for predicting the durability of antibody responses to vaccines.

[0152] method Previously cryopreserved PBMCs from humans and rhesus monkeys were thawed, washed once with PBS, and stained with the appropriate antibody cocktail in 100 μl of PBS containing 1.5 μM SYTO™ RNASelect™ Green fluorescent cell stain (S32703, Invitrogen) at room temperature. After 20 min, 300 μl of 1% paraformaldehyde was added directly to the samples. On the same day, cells were analyzed on a FACS Symphony flow cytometer (BD Biosciences). The FSC value threshold was set at 4000 to ensure visualization of the platelet population. Analysis of flow cytometry files was performed using FlowJo software (FlowJo, LLC). For identification of PBMC-free platelets in thawed human PBMCs, a cocktail of anti-CD3-BUV737, anti-CD19-APC, anti-CD14-BV605, anti-CD56-PE, and anti-CD41-BV421, anti-CD61-PE-Cy7 antibodies was used. Platelets are CD3 + , CD19 + , CD14 + and CD56 + CD41 after cell exclusion + CD61 +To identify PBMC-free platelets in thawed NHP PBMCs, a cocktail of anti-CD3-PE-CF594, anti-CD8-BUV563, anti-CD20-BUV737, anti-CD14-BUV805, and anti-CD41-BV421, anti-CD61-PE-Cy7 antibodies was used. Platelets were CD3 + , CD8 + , CD20 + and CD14 + CD41 after cell exclusion + CD61 + was defined as a cell.

[0153] Mouse blood anticoagulated with acid citrate dextrose solution (sc-214744, Santa Cruz Biotechnology, Inc.) was centrifuged at 150×g for 10 min at room temperature in a ratio of 6-8:1 to obtain platelet-rich plasma. 20 μl of freshly prepared platelet-rich plasma was mixed with 80 μl of PBS containing 1.5 μM RNASelect™ dye for 20 min at room temperature along with 0.6 μl of anti-TER119-PE, anti-CD41-BV421, and anti-CD61-PE-Cy7 antibodies. Mouse platelets were collected by centrifugation at 100°C for 20 min at room temperature using 0.5% PBS containing 1.5 μM RNASelect™ dye. + CD41 after erythrocyte exclusion + CD61 + was defined as a cell.

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[0245] The foregoing merely illustrates the principles of the present invention. It is understood that those skilled in the art can devise various arrangements that embody the principles of the present invention and are within the spirit and scope of the present invention, although not expressly described or shown herein. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts contributed by the inventor to further advance the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein that describe principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Thus, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims.

[0246] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 210,794, filed June 15, 2021, the entire disclosure of which is incorporated herein by reference in its entirety.

[0247] Federal Funding Statement This invention was made with Government support under Contract AI090023 awarded by the National Institutes of Health. The Government has certain rights in this invention.

Claims

1. A method for determining whether a candidate adjuvant provides a core response substantially similar to that of a high-performance reference adjuvant, comprising: administering a vaccine comprising the candidate adjuvant to a mammal; determining a core response signature from immune cells; predicting whether the candidate adjuvant induces the core response based on a day 1 change in expression. A method comprising the above.

2. The method according to claim 1, wherein the core response comprises expression in one or more of TGM2, ANKRD22, and KREMEN1.

3. The method according to claim 1 or 2, further comprising selecting a candidate adjuvant for clinical use or development, wherein the candidate adjuvant is substantially similar to a high-performance reference adjuvant.

4. The method according to claim 1 or 2, wherein the mammal is a mouse, a non-human primate, or a human.

5. A method for predicting the persistence of an immune response to a candidate vaccine, comprising: administering the candidate vaccine to a mammal; determining a signature response from a sample containing immune cells in the mammal; predicting the persistence of the response from the signature response. A method comprising the above.

6. The method according to claim 5, wherein the candidate vaccine comprises an adjuvant.

7. The method according to claim 5 or 6, wherein the candidate vaccine is an mRNA vaccine.

8. The method according to claim 5 or 6, wherein the candidate vaccine is a viral vector vaccine.

9. The method according to claim 5 or 6, wherein the candidate vaccine is a live or inactivated virus vaccine.

10. The method according to claim 5 or 6, wherein the sample containing the immune cells is obtained 7 to 10 days after administration of the candidate vaccine, and the signature response is compared with the baseline pre-administration value.

11. The method according to claim 5 or 6, wherein the sample containing the immune cells is a peripheral blood mononuclear cell sample (PBMC) containing platelets.

12. The method according to claim 5 or 6, wherein the sample containing the immune cells is a platelet-rich plasma sample.

13. The method according to claim 5 or 6, wherein determining the signature response includes one-step flow cytometry analysis of platelet RNA content, and an increase in platelet RNA predicts a sustained response.

14. Determining the signature response comprises (a) labeling the cells present in the sample with a reagent that distinguishes platelets from other cells in the sample; (b) labeling the cells present in the sample with RNA-selective staining; (c) analyzing the sample by flow cytometry gated on platelets to determine RNA content and the method according to claim 5 or 6.

15. The reagent for distinguishing platelets from other cells in the sample comprises an antibody specific for CD41 and an antibody specific for CD61, and platelets are CD41 ＋ CD61 ＋ and the method according to claim 14.

16. The method according to claim 15, wherein the reagent for distinguishing platelets from other cells in the sample further comprises one or more of anti-TER119, anti-CD3, anti-CD8, anti-CD19, anti-CD20, anti-CD14, and anti-CD56 antibodies.

17. The method of claim 13, wherein a sustained immune response is associated with an increase in RNA content of at least about 5-fold compared to baseline. **Claim 18** The method of claim 5, wherein the signature response comprises expression level data from one or more genes selected from GPR15, EPS8L1, SLC38A1, GXMM, MGLL, CTTN, XK, PF4, SELP, CDHR5, MYLK, CALD1, CXCL9, SDPR, SPTB, PROS1, PRKAR2B, PPBP, CXCL5, HEMGN, EGF. **Claim 19** The method of claim 5 or 18, wherein the signature response is determined from a sample obtained about 1 to 7 days, or about 10 days, after a secondary or primary immunization. **Claim 20** The method of claim 5 or 6, further comprising selecting a candidate adjuvant for clinical use or development, wherein the candidate adjuvant induces a signature indicative of a sustained antibody response. **Claim 21** The method of claim 5 or 6, wherein the mammal is a mouse, a non-human primate, or a human.

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