Molecular signatures of vaccine adjuvants

Molecular signatures of vaccine adjuvants

G Model ARTICLE IN PRESS JVAC-16431; No. of Pages 6 Vaccine xxx (2015) xxx–xxx Contents lists available at ScienceDirect Vaccine journal homepage...

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ARTICLE IN PRESS

JVAC-16431; No. of Pages 6

Vaccine xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

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Molecular signatures of vaccine adjuvants Thorunn Olafsdottir, Madelene Lindqvist, Ali M. Harandi ∗ Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 7A, Gothenburg, Sweden

a r t i c l e

i n f o

Article history: Available online xxx Keywords: Vaccine adjuvants Systems biology Omics Mode of action of adjuvants

a b s t r a c t Mass vaccination has saved millions of human lives and improved the quality of life in both developing and developed countries. The emergence of new pathogens and inadequate protection conferred by some of the existing vaccines such as vaccines for tuberculosis, influenza and pertussis especially in certain age groups have resulted in a move from empirically developed vaccines toward more pathogen tailored and rationally engineered vaccines. A deeper understanding of the interaction of innate and adaptive immunity at molecular level enables the development of vaccines that selectively target certain type of immune responses without excessive reactogenicity. Adjuvants constitute an imperative element of modern vaccines. Although a variety of candidate adjuvants have been evaluated in the past few decades, only a limited number of vaccine adjuvants are currently available for human use. A better understanding of the mode of action of adjuvants is pivotal to harness the potential of existing and new adjuvants in shaping a desired immune response. Recent advancement in systems biology powered by the emerging cutting edge omics technology has led to the identification of molecular signatures rapidly induced after vaccination in the blood that correlate and predict a later protective immune response or vaccine safety. This can pave ways to prospectively determine the potency and safety of vaccines and adjuvants. This review is intended to highlight the importance of big data analysis in advancing our understanding of the mechanisms of actions of adjuvants to inform rational development of future human vaccines. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Secrets to be unraveled by omics Conventional immunological approaches such as serology and cellular immunology have been successfully employed to study immune responses triggered by human vaccines, and as such confer significant insights into the vaccine-induced immune responses. Systems biology is an emerging inter-disciplinary field of studying complex interactions within biological systems in a holistic manner by the means of mathematical and computational modeling. Integration of multiple layers of data, derived from distinct ‘omics’ such as transcriptomics, proteomics, and metabolomics using various platforms has the potential to provide an in depth understanding of the complex mechanisms underlying immune responses induced by vaccines and adjuvants. Further, such multi-omics approach offers an unprecedented opportunity to identify early signatures/biomarkers predictive of magnitude, quality and/or longevity of the vaccine/adjuvant-induced adaptive immune responses as well as efficacy and safety of vaccines.

∗ Corresponding author. Tel.: +46 31 7786229. E-mail address: [email protected] (A.M. Harandi).

Recently, systems biology approach has been employed in vaccine research where data obtained from different omics and conventional immunological read outs were integrated to decipher the mode of actions of human vaccines. The first pioneering study of the vaccine-induced immunity through a systems biology approach was conducted in healthy adults vaccinated with the live attenuated yellow fever vaccine YF-17D, where early predictive signatures of later CD8 T cell- and B cell-responses were identified [1]. The study of yellow fever vaccine has been elegantly followed up by a large-scale study, analyzing blood transcriptome profile of five human vaccines, addressing the key question of whether there are universal predictors of vaccine efficacy. Distinct transcriptional signatures were found to correlate with vaccine-specific antibody responses, suggestive of vaccine-specific responses, rather than a universal signature across vaccine types [2]. The snap shot of migrating cells and molecules observed in the blood can be complemented with the analysis of the immune response in the lymphoid tissues. Animal models can provide the opportunity of profiling biomarkers in the lymphoid and mucosal tissues in addition to the blood and as such can help identifying blood biomarkers that reflect immune response status in the lymphoid tissues and target organs. The importance of evaluating transcriptional changes induced by adjuvants in vivo has been

http://dx.doi.org/10.1016/j.vaccine.2015.04.099 0264-410X/© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4. 0/).

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recently shown for the two human adjuvants Alum and MF59. While these two adjuvants did not induce any appreciable transcriptional changes in splenocytes in vitro, intramuscular (i.m.) injection triggered significant changes in gene expression at the site of injection [3]. Nevertheless, two recent papers have reported conflicting results on how well genomic responses in mice could mimic that of human. Seok et al. [4] reported a poor correlation between mouse and human transcriptomics profiles in inflammatory diseases. A report from Takao et al. [5], on the other hand, reported that mouse models closely captured the transcriptomics profile of humans when the same datasets were re-analyzed using a different statistical analysis approach. Seok et al. selected genes for correlation analysis based solely on genes that were found differentially expressed in humans whereas Takao et al. based their analysis on genes that were differentially expressed in both mice and humans Further differences in the methodology of the two papers included the choice of correlation analysis methods and different treatments of time-course data, resulting in two contradicting conclusions derived from the same original datasets. This exemplifies how the choice of methodologies used for the analysis of transcriptomics data can result in different conclusions, emphasizing the importance of a careful selection of data analysis approaches. Systems biology has recently been used to study the mode of actions of vaccine adjuvants. Below we will discuss a handful of publications currently available on the transcriptome profiling following in vivo administration of adjuvants in animal models (summarized in Table 1).

2. Mode of actions and signatures of adjuvants used in human vaccines With some billion doses of alum-adjuvanted vaccines given to humans since its discovery in 1920s, alum is the most widely used adjuvant in human vaccines and as such is often included as a benchmark in vaccine adjuvant research. Nevertheless, its multifaceted mechanisms of action have only recently been studied [6]. These studies indicate that alum adjuvanticity is mediated by an increase in antigen uptake, induction of danger signals, and recruitment of numerous types of immune cells [7]. The lack of uniformity in the type of alum and immunization protocols used across different mode of action studies has however yielded different results such as the role of NALP3 inflammasome in alum mediated adjuvanticity [8,9]. MF59 developed by Novartis (ex-Chiron) represents the first adjuvant used in human vaccines in the post-alum era. MF59 is an oil-in-water emulsion containing the fully metabolizable oil squalene, Tween 80 (a water-soluble surfactant) and Span 85 (an oil-soluble surfactant) that induces a strong antibody response [10]. MF59 has been shown to enhance the diversity and affinity of the antibody response to influenza vaccination in humans [11]. Extensive immunological and transcriptome analyses have been performed on MF59 to evaluate its mode of action, making it one of the best characterized adjuvants so far [3,10,12,13]. The mode of action of MF59 has been compared with that of alum, providing valuable information on common as well as unique features of these two antibody inducing adjuvants [3,13]. Whole transcriptome analysis on mouse quadriceps following i.m. injection of MF59 or alum revealed that MF59 induces transcriptional changes of almost three-times as many genes as alum at the injection site and that the expression of only 34 genes were exclusively changed by alum [13]. Common genes whose expression significantly changed by both of the two adjuvants included cytokines and cytokine receptors genes together with genes involved in antigen processing and presentation. However, MF59 was found to induce a stronger

and quicker gene expression compared to alum for the majority of the common up-regulated genes. Among exclusive genes whose expression up-regulated in the muscle fibers by MF59 were early biomarkers such as the transcription factor JunB (involved in regulating gene activities) and pentraxin 3 (involved in inflammatory responses). Similar to MF59 alone, MF59 in combination with the flu antigen was shown to be a strong modulator of transcripts locally in the muscle with no significant changes in the draining lymph nodes [3]. Transcriptional profiles of MF59 indicated that it induced IFN-type I independent adjuvanticity that was confirmed by the finding that in vivo administration of IFN- type I receptor blocking antibody did not influence the immune enhancing effect of MF59. Adjuvant systems 03 and 04 (AS03 and AS04) developed by GSK are included in few licensed human vaccines [14–17]. AS03 is a squalene oil in water emulsion containing ␣-tocopherol (a form of vitamin E) and polysorbate 80. AS04 consists of alum and the TLR 4 agonist monophosphoryl lipid A. Both adjuvants were reported to activate NF-␬B, a master transcription factor of innate immune response, in a transgenic NF-␬B luciferase reporter mouse model [18,19]. The NF-␬B activation was restricted to the site of injection and the draining lymph nodes and no activation was observed in the remote draining lymph nodes demonstrating a localized response to the adjuvants. Similar to another oil-in-water emulsion based adjuvant MF59, AS03 induced expression of higher number of genes of the immune cell recruiting chemokines and pro-inflammatory cytokines than alum [18].

3. Mode of actions of exploratory adjuvants in or close to clinic The potential of the TLR ligands as vaccine adjuvants has been extensively explored [20–23]. The TLR9 agonist unmethylated cytidine–phosphate–guanosine (CpG) oligodeoxynucleotides are much studied adjuvant candidates both in preclinical and clinical settings [24]. Microarray analysis of spleen cells following intraperitoneal (i.p.) injection of mice with CpG identified several major inducers of gene network regulation involved in inflammatory responses, including TNFa, IL1a, IL1b and IFNg at 3 h [25]. This was followed by up-regulation of genes for suppressor of cytokine signaling 1 (SOC1) and SOC3 molecules along with IL10 at 24 h with a possible role in controlling the inflammatory response. In a comparative mode of action study of CpG, MF59 and alum, a set of “adjuvant core responsive genes” was identified at the site of injection following i.m. immunization [13]. Functional analysis on these common genes revealed enrichment of cytokine-cytokine receptor interactions, host-pathogen interaction and defense immunity protein activity. The TLR 7/8 ligand, Resiquimod R848 and the TLR1/2 ligand Pam3CSK4 were also evaluated following i.m. administration in mice [3]. R848 was shown to strongly modulate interferon-related genes as well as a broad activation in the draining lymph nodes observed in cytokine and interferon-related genes. However, much fewer interferon related genes were induced by Pam3CSK4 and the effect on the draining lymph nodes was only modest compared to that of R848 [3]. It is however noteworthy that dendritic cells in mice and human possess distinct patterns of TLR expression that can limit the translation of data from mice to humans at least for TLR-based adjuvants [26]. Recently, transcriptome profiles of the TLR ligands (TLR-L) MPL (TLR4-L), Resiquimod R848 (TLR7/8-L) and CpG (TLR9-L) were compared in rhesus macaques [27]. All TLR-Ls induced rapid and robust expansion of neutrophils in the blood as well as expansion of CD14+ monocytes. However, the different TLR adjuvants also showed distinct signatures of early innate responses in blood and the draining lymph nodes with the main differences observed in their impact

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Table 1 Summary of main molecular signatures of vaccine adjuvants identified by transcriptomics in mice. Adjuvant

Compound

Methodology

Route

Organ evaluated by omics

Main functional analysis/gene clusters

Main molecules detected on transcriptome level

Refs.

Alum

Mineral salt

Microarray

i.m.

Antigen processing and presentation, Cytokines activity, Cytokine binding

O/W emulsion

Microarray

i.m.

MHCII genes, Ccl2, Ccl4, Ccl5, Ccl12, Cxcl10, IL1b, IL12, Cytokine receptors, MHCI genes Ptx3, JunB, MHCII genes, MHCI genes, CD11b, Selp, Cathepsins, B2m, Il1a, Il1b, Il1rn, Il1f9, Il5, Il4, Il9, Ccl2, Ccl4, Ccl5, Ccl12, Cxcl10, IL12, Ccr1, Cxcr4, Ccr5, Ccr2, Ccr,3 receptors for IL1, IL2, IL4 and IL10

[3,13,18,29]

MF59

Quadriceps, Tibialis muscle, Inguinal LNs Iliac LNs Quadriceps Inguinal LNs

ASO3

O/W emulsion containing ␣tocopherol

Low density arrays Real time PCR

i.m.

Tibialis muscle and iliac LNs

[18]

CpG

TLR9 ligand

Microarray

i.m. i.vag. i.p.

Quadriceps Vaginal tissue Spleen

Antigen processing and presentation, Cytokine activity, Cytokine binding, Inflammatory response, Chemotaxis Activation of complement pathways, Platelet activation, Proteasome activation, MHC class I peptide loading N/A

R848

TLR7/8 ligand

Microarray

i.m.

Quadriceps Inguinal LNs

Interferon type I and II signaling Cytokines

Pam3CSK4

TLR2/1 ligand

Microarray

i.m.

Quadriceps Inguinal LNs

GLA-SE

TLR4 ligand

Microarray

i.m.

Quadriceps, Iliac and inguinal LNs Whole blood

␣GalCer

NKT cell agonist

Microarray

i.vag.

Vaginal tissue

IFNg, Ccl6, Ccl20, Cxcl1, Cxcl10, Cxcl9, Cxcl11, Cxcl3, C3,

[31]

LT-K63

Bacterial toxoid

Microarray

Intrapulmonary

Lung

Leukocyte transendothelial migration, IFN type I-independent mechanism MyD88 and TRIF dependent signaling pathways, Cytokines, Chemokines, Cytokine receptors and signaling molecules, Complement and antigen presentation Metabolic processes, Immune cell activation Activation of complement pathways, Platelet activation, Proteasome activation, MHC class I peptide loading, Inhibitory receptor PD-1 signaling Pathogen recognition, inflammation, chemokine receptor genes, chemokines, antiviral genes, complement cascade genes, C-type lectins, oxidative stress-related genes, B- and T-cell signature genes

Ccl3, Ccl2, Ccl7, Cxcl1, Csf3, Cxcl5, Cxcl2, Cxcl9, Cxcl10, Ccl3, Ccl4, Ccl5IL6, IL1b, IL1a, TNFa, IFNg MHCI genes, MHCII genes, Ptx3, JunB, Ccl2, Ccl4, Ccl5, Ccl12, Cxcl1, Cxcl2, Cxcl10, Cxcl9, Cxcl13 IL1b, IL12 C3, IFNg, IL6, TNFa, MHC I genes, Ccl2, Ccl12, Cxcl1, Cxcl10, Cxcl11, IL-10, IFNg, TNFa, IL-6, IL18, MYC, IL15, Tnfsf10, NFkB1, IL1a, IL1b, Stat1, Stat2, IL10, NFkBIA, IL1RN1, SOCS1, SOCS3, FOS Cxcl10, Cxcl11, Cxcl13, Tnfsf10/Trail, Ifna2, Ifna4, Ifna12, Ifnb1, IFNg, Il12b, CD69, CD86, CD40 Chemokines, IL1 family cytokines, Itgam, Icam1, Vcam1, Selp Cxcl1, Cxcl2, Cxcl5, Cxcl9, Cxcl10, Ccl2, Ccl3, Ccl4, Cxcl11, Ccl6, Ccl9, IL6, IFNg, Ccr1, Ccr2, Csf1r, MHCI and MHCII genes, FcgR1

PGR1, IL18bp, prostaglandin E receptor, MHCII II genes, Ccr5, Ccr2, Cxcr6, Ccl6, Ccl8, Cxcl13, Ccl5, Xcl1, Oas2, C1q, CYBB, VH , VL , IgG H chain region, TCRb, Cd8a, Tcf7, Lck, Itk, Ccl9, Ccl2, Ccl4, Ccl3, Ccr1, IL6, IL1b, Cxcl10

[37]

on different dendritic cell subsets and monocytes. It is hence of paramount importance to carry out comprehensive cross-species studies to pinpoint if and how different animal models could mimic human response to candidate vaccine adjuvants. GLA-SE developed by Infectious Disease Research Institute (IDRI) in Seattle is a two-component adjuvant consisting of a synthetic TLR4 agonist referred to as Glucopyranosyl Lipid A (GLA) and squalene-oil emulsion known as stable emulsion (SE). GLASE has been tested in clinical trials as part of exploratory vaccines against tuberculosis, malaria, HIV and other diseases [28]. Transcriptome profiling of the site of injection, draining lymph nodes and blood at several early time points provided important information on the kinetics and magnitude of gene expression induced by

Antigen processing and presentation, Cytokines activity, Cytokine binding, Leukocyte transendothelial migration, IL-1 signaling pathway, Prostaglandin synthesis, Inflammatory response, IFN type I-independent mechanism N/A

[3,12,13]

[13,25,31]

[3]

[3]

[29]

GLA-SE [29]. The overall duration and magnitude of gene expression at the injection site, cytokine responses in serum as well as leucocyte recruitment to the muscle and the draining lymph nodes were more profound after GLA-SE administration (peaking at 48 h) compared to those of the aqueous GLA formulation (peaking at 6 h) whereas SE alone (peaking at 96 h) induced only limited gene expression. GLA-SE induced more profound gene expression at the site of injection than in the draining lymph nodes and blood. Most of the transcripts up regulated in the lymph nodes were shared with those up regulated in the muscle (54–70%) whereas transcriptional profile of the blood represents a more unique transcript pattern. The most highly induced genes belonged to chemokines, cytokine

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receptors, signaling molecules, complement and antigen presentation pathways. Transcriptional profiles induced by aqueous GLA and GLA-SE, including Cxcl9, Cxcl10, Cxcl11, Ifng, Stat1 and Ccr5 as well as Th1-associated serum cytokines CCL2, CXCL1, TNF␣, IL-6, IFN-␥ and CSF3 corroborated the documented Th1-baising property of these adjuvants [30]. Genes involved in MyD88 and TRIF dependent pathways were also up regulated by both GLA-SE and GLA [29]. This is in line with a recent finding in which the Th1 polarizing effect of GLA-SE was shown to be dependent on both MyD88 and TRIF signaling pathways [28]. It would hence be important to identify early transcriptome biomarkers of different classes of adjuvants that could help predicting the type of adaptive immune response that they could mobilize later.

transcriptional changes with cellular readouts performed by FACS [37]. Although nasal delivery of LT-K63 is no longer a viable option owing to the increased risk of facial nerve paralysis (Bell’s Palsy) observed in few human subjects [38], the wealth of the data generated on the immunological and transcript signatures of this adjuvant can inform the rational development of potent and safe mucosal adjuvants. It however remains elusive how to uncouple the adjvanticity from reactogenicity. It is likely that the recent advances in the discovery of small molecule immune potentiators [39] combined with systems biology approach could enable the development of adjuvants with potent immunostimulatory property and devoid of excessive reactogenicity.

5. Opportunities and challenges 4. Mode of actions of exploratory mucosal adjuvants Most human pathogens invade the body through and/or establish infection in the mucosal tissues thus making mucosal immunization an attractive approach to counter mucosally transmitted infections. However, caution should be exercised for using adjuvant powerful enough to elicit mucosal immune response while not causing untoward effect on the delicate mucosal tissue itself. In this respect the discovery of biomarkers predictive of excessive inflammatory responses in the mucosal tissues can provide important information for the development of safe and effective mucosal adjuvants. Our group has recently conducted a genome-wide transcriptome analysis of the murine vaginal tissue following local administration of CpG and the agonist of invariant natural killer T cells alpha-Galactosylceramide (␣-GalCer), two exploratory adjuvants which induce comparable protective immunity in mice against genital herpes infection when administered together with herpes simplex virus type 2 glycoprotein D [31–33]. Integrated bio-functional analysis revealed “inflammatory response” as the main pathway induced by both adjuvants, although CpG was shown to induce more pronounced and persistent transcriptional changes with four times as many genes involved in inflammatory responses up-regulated compared to ␣-GalCer. Ifng gene was identified as the main inducer of the inflammatory pathway induced by both adjvuvants, while Tnfa, Il6 and Il1b genes were solely up-regulated by CpG [31]. The excessive inflammatory response induced in the vaginal mucosa by CpG may explain the previously observed damage to the vaginal epithelium by CpG [34]. Furthermore, ␣-GalCer, but not CpG, induced up regulation of genes involved in inhibitory receptor programmed death 1 (PD-1) signaling that has been shown to be involved in controlling excessive inflammation [35]. Therefore, it is likely that the expression of these genes can at least partly explain the more controlled inflammatory response observed following ␣-GalCer administration. Comprehensive analysis of shared and distinct networks and genes induced by different adjuvants at the mucosal sites and the draining lymph nodes can reveal the desired characteristics of adjuvants to be included in mucosal vaccines. LT-K63, a derivative of heat labile enterotoxin of E. coli (LT), is a much studied exploratory mucosal adjuvant [36]. A transcriptome profiling of lung tissue combined with flow cytometric analysis was performed in mice following intrapulmonary administration of LTK63 [37]. While no LT-K63 specific transcriptional changes could be detected at early time points (3–12 h), LT-K63 specific gene up regulation was detectable around one week after vaccination and those included genes involved in pathogen recognition, oxidative burst, inflammation, antigen presentation and recruitment of cells from both the innate and adaptive arms of the immune system. Late transcriptional changes (14 days following immunization) were more specific for B cells and the authors could correlate the

Due to the availability of analytical tools and low cost, all studies reported so far on systems biology of adjuvants employed microarray for transcriptomics analysis. High throughput RNA sequencing offers several advantages over microarray, including superiority in detecting low abundance transcripts and allowing identification of genetic variants [40]. Important for vaccine research, RNA sequencing has also the advantage of providing information on the individual make up of the adaptive immune responses such as sequences of HLA and TCR molecules [41]. Metabolomics has emerged as an important omics approach to identify and quantify metabolites resulting from chemical processes involved in metabolism. Metabolites are small molecules with low molecular weight (<1500 Da) that are intermediate or end products of biological processes in the body [42]. Metabolome is affected both by internal factors and environmental stimuli and has therefore been suggested to play a significant role in bridging the phenotype–genotype gap. The metabolite profiling is deemed to complement the up-stream transcriptome and proteome of biological systems, which are subject to epigenetic regulation or post-translational modification, respectively [43]. Recent advancements in mass spectrometry have enabled the detection of thousands of metabolites that can be measured either in a targeted or untargeted manner [43]. Untargeted metabolomics aims at measuring as many metabolites as possible in an unbiased way to provide information on metabolome at system level and as such can serve as an important component of systems biology analysis. However, analysis of complex datasets generated by untargeted metabolomics required for identification of actual metabolites represents a challenge. Targeted metabolomics, on the other hand, aims at studying a pre-defined set of metabolites in a hypothesis driven manner. While metabolomics has been successfully used to decipher metabolite signatures of human diseases [44–47], very little is known on the metabolomics of vaccines. A recent metabolomics study of sera obtained from DNA immunized mice revealed that metabolites involved in the pathways of lipid metabolism were significantly increased in the sera of the DNA immunized mice, which was speculated as a potential safety concern due to the possible risk of atherogenesis [48]. Although challenging, the integration of multiple layers of information derived from different ‘omics’ can provide an in depth understanding of the complex mechanisms of actions of vaccines and adjuvants. It is envisaged that information derived from the integrated omics combined with immunological and clinical data could help rational development of novel adjuvants for human use with improved potency and safety (Fig. 1). There are however several challenges to be negotiated. The employment of a variety of protocols in generating data has hampered a direct comparison of the mode of actions of different vaccine adjuvants. Therefore, there is a need to standardize and cross-validate the mode of action studies of adjuvants using standardized and harmonized protocols

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Fig. 1. A schematic diagram representing different complementary approaches that can help unraveling the mode of actions of vaccine adjuvants.

and reagents [49]. This unmet need is now being addressed as part of the High Impact Project Advanced Immunization Technologies (ADITEC), a 5-year project funded by the European Commission. Through employment of the cutting edge omics technology combined with the state-of-the-art immunological readouts, ADITEC is currently studying the mode of actions of promising adjuvants that are already in clinic using standardized protocols with the objective of discovering early biomarkers predictive of adjuvant potency and safety. It is envisaged that the discovery of core and distinct molecular signatures among different adjuvants predictive of different immune responses can inform rational development of safe and potent adjuvants for human use. Besides current work within the European project ADITEC, other adjuvant research programs funded through the National Institute of Allergy and Infectious Disease in the United States are underway. These initiatives focus on both understanding the mode of actions of adjuvants as well as enhancing the pipeline of new adjuvant candidates for further development. Another important challenge is that the overwhelming amount of data generated by different “omics” is of limited value unless they are integrated and contextualized followed by validation in relevant animal models and human studies [50]. However, the use of knock-out mouse models may not be feasible for validating a large number of biomarkers, and besides mice may not be the model of choice for several candidate molecules. More plausible approach might include employment of gene silencing approaches such as the use of short hairpin RNA (shRNA) vectors or short interfering RNA (siRNA) molecules [51]. Another emerging technology

for genome editing is the use of the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system through which specific genes are completely knocked out [52]. Further, significant advances in single-cell analysis such as tetramer sorted, single cell transcriptional analysis and mass cytometry that allows the analysis of as many as 100 parameters per cell enable the validation of biomarkers at cellular level in the landscape of immune responses [53,54]. Many questions however remain unanswered. This includes what are the molecular signatures of adjuvants that can induce high avidity antibodies, enhance the breadth of the epitopes involved in antibody response, induce the desired type of T cell response, and most importantly induce long-term memory. Based on the rapid pace of investigations in the systems biology field powered by the emerging cutting edge technologies along with the advancements in high throughput immunological techniques it is likely that the answer to these and other important questions will soon be forthcoming.

Acknowledgements This work was supported by the European Commission-FP7 grant Advanced Immunization Technologies (ADITEC) under grant agreement no. FP7-HEALTH-F2010-280873, Innovative Medicines Initiative (IMI)-supported project BioVacSafe, Fondation Dormeur, Vaduz, Sahlgrenska Academy returning grant and Konrad and Helfrid Johansson Foundation.

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Please cite this article in press as: Olafsdottir T, et al. Molecular signatures of vaccine adjuvants. Vaccine (2015), http://dx.doi.org/10.1016/j.vaccine.2015.04.099