Author response: Identification of potential biomarkers of vaccine inflammation in mice

Field: Immunology and Microbiology (Life Sciences)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Systems vaccinology approaches have been used successfully to define early signatures of the vaccine-induced immune response. However, the possibility that transcriptomics can also identify a correlate or surrogate for vaccine inflammation has not been fully explored. We have compared four licensed vaccines with known safety profiles, as well as three agonists of Toll-like receptors (TLRs) with known inflammatory potential, to elucidate the transcriptomic profile of an acceptable response to vaccination versus that of an inflammatory reaction. In mice, we looked at the transcriptomic changes in muscle at the injection site, the lymph node that drained the muscle, and the peripheral blood mononuclear cells (PBMCs)isolated from the circulating blood from 4 hr after injection and over the next week. A detailed examination and comparative analysis of these transcriptomes revealed a set of novel biomarkers that are reflective of inflammation after vaccination. These biomarkers are readily measurable in the peripheral blood, providing useful surrogates of inflammation, and provide a way to select candidates with acceptable safety profiles. https://doi.org/10.7554/eLife.46149.001 eLife digest Measles, whooping cough and other diseases can cause serious illness and death in humans, especially in young children and other vulnerable individuals. Giving people vaccines ‘trains’ their immune system to recognize and fight the microbes that cause the conditions. During an infection, the immune system triggers a set of responses that limit the spread of the infectious agent and eliminate it from the body. This can include swelling of tissues (known as inflammation), which in rare cases, can be life threatening. Inoculations work by sparking a mild immune response in the body. Before a new vaccine is licensed for use, it is thoroughly tested in mice and rodents, and then in human volunteers, to ensure it will cause little or no inflammation. Finding a way to predict early on whether a vaccine candidate will trigger dangerous levels of inflammation would improve this process. To explore this, McKay, Cizmeci et al. injected the muscle tissue of different groups of mice with one of four licensed vaccines which, by definition, cause little or no inflammation. Other groups of animals were given one of three drugs known to trigger inflammation. Over the following seven days the team repeatedly collected blood as well as cells from the muscle tissue and the lymph nodes. These samples were then analysed to find out which genes were switched on or off at any given time. The experiments show that the responses of genes in the blood and lymph cells of the mice are connected to those in the muscle cells. Therefore, blood samples may provide a quick and convenient way to assess how an animal is responding to a potential new vaccine. By comparing the genes switched on or off in response to the different vaccines and drugs, McKay, Cizemeci et al. were able to identify a set of genes (known as “biomarkers”) that are associated with inflammation in animals. These biomarkers can be used to spot early on whether a new treatment is triggering inflammation. The next step would then be to identify a similar or identical set of biomarkers in other animals used in vaccine research, and in humans. Ultimately, this approach could make the assessment of the safety of a new vaccine candidate easier. https://doi.org/10.7554/eLife.46149.002 Introduction Systems biology approaches are increasingly being used to describe and define signatures of immunity, initially in the setting of infection but more recently in vaccine-induced responses, leading to the development of systems vaccinology (Querec et al., 2009; Olafsdottir et al., 2015). These transcriptomic analyses have primarily focused on the prediction of vaccine efficacy and immune outcome with the assessment of vaccine safety and potential reactogenicity relying on clinical reactive scores of adverse events (Bucasas et al., 2011; Furman et al., 2013; Obermoser et al., 2013; Vahey et al., 2010; Li et al., 2014; Leonardi et al., 2015; Vesikari et al., 2009). However, the transcriptome can also reveal the intricate details of the very early events after vaccination. We have utilized this approach to examine this initial innate response and any subsequent inflammation resulting from a vaccination, to identify biosignatures that have correlative or surrogate potential for vaccine-related inflammation and safety. It is likely that the activation and maturation of the fundamental responders within the immune system will follow set developmental patterns, which may be revealed by an examination of the transcriptome. Thus any potential differences between each vaccine would probably be due to the degree of response, and to the involvement of different populations of central immune system players and of accessory cells. The dominant determinants of such responses are the nature of the vaccine antigen, its formulation, and the presence or absence of molecularly defined adjuvants (Li et al., 2017; O'Gorman et al., 2015). The blood and lymphatic systems are the primary routes of transport of cells and chemical signals from one immunological organ to another, with immune cells and soluble mediators trafficking between the injection site and the local draining lymph node (Girard et al., 2012; Jackson, 2014). Indeed, proteins and molecules that can be measured in the blood after vaccination are likely to be useful biomarkers of such responses as blood draws can be readily taken from most individuals. Although a particular cell or inflammatory molecule may be present in relatively high numbers and concentrations in both the lymph node and the injection site, it is apparent that the large volume of blood in the circulatory system may dilute this molecule and prevent detection. In this study, we examined the molecular signatures generated in the blood, the draining lymph node and the injection site in the muscle of mice after the administration of a number of licensed vaccines, which set the benchmark for what is safe, and of a set of known agonists of Toll-like receptors (TLRs) or adjuvants, to set an upper boundary of inflammatory response (Haziot et al., 1998; Qureshi et al., 1999; Akira and Takeda, 2004; Fitzgerald et al., 2004; Matsumoto and Seya, 2008). The four licensed vaccines used in this study have a substantial history of safe use in humans, with large datasets supporting their safety and tolerability. They ranged from protein alone to vaccines that included adjuvants or a known component that have an acceptable degree of reactogenic potential (whole cell pertussis) (Frey et al., 2003; Tregnaghi et al., 2012; Mohanty et al., 2018; Van Den Ende et al., 2017). We also used the water-in-oil emulsion incomplete Freunds adjuvant (IFA) (Montanide ISA 51) and two TLR agonists (lipopolysaccharide (LPS) and Poly I:C) (Table 1). A set of indicators of vaccine-elicited inflammation in a small animal model will provide an early warning system that can be used to inform decisions on the potential tolerability and ultimately the utility of vaccines undergoing selection and development, and will provide additional tools to de-risk late-stage failure. Table 1 Vaccines and inflammatory agents. The vaccines, TLR agonists, adjuvant and saline used in the present study are detailed. Vaccines were administered by injection of 1/10th the human dose in a 50 µL volume. TLR agonists, IFA and saline were also given in a single 50 µL volume. All injections were into the mouse hind leg quadriceps muscle. https://doi.org/10.7554/eLife.46149.003 Vaccine/TLRComponentsAbbreviationManufacturerPentavac SDDiphtheria, tetanus, pertussis (whole cell), hepatitis B (rDNA) and haemophilus type b conjugate vaccinePentavacSerum Institute IndiaAgrippalTrivalent flu subunits – H3N2, H1N1 and influenza BTri-FluSeqirusFluadTrivalent flu subunits – H3N2, H1N1 and influenza B + MF59Tri-Flu + MF59SeqirusEngerix BRecombinant hepatitis B sAg absorbed on alumGlaxoSmithKlineIFAMontanide ISA 51 VGSeppicLPSLPS-EB UltrapureInvivogenPoly I:CPolyinosinic:polycytidylic acidSigma Results Transcriptomic profiles in muscle, draining lymph nodes and PBMCs of mice treated with different licensed vaccines and TLR agonists High-quality RNA samples, isolated from muscle tissue excised around the injection site, the draining medial iliac lymph nodes (MLN) and total peripheral blood mononuclear cells (PBMCs), were taken at 4, 8, 24, 48, 72, 168 hr after the vaccination or treatment with LPS, polyI:C or IFA (Table 1) and subjected to genome-wide transcriptome analysis. The unvaccinated mice, and mice receiving saline alone, were used as control groups. Differentially expressed genes were defined as those with a significant (Benjamini-Hochberg (BH) adjusted p-value<0.01) change in expression in a vaccinated group when compared to the unvaccinated group and the saline group at a given time point. Violin plots that chart the numbers of upregulated and downregulated differentially expressed genes revealed that the immunisations differed in terms of the magnitude and the kinetics of the transcriptomic responses induced in muscle, MLN, and blood (Figure 1). Strong responses were observed in the injected muscle tissue for all of the immunisations, with the highest levels of fold change and of absolute numbers of differentially expressed genes being detected at 72 and 168 hr after treatment. Pentavac SD, Poly I:C and particularly LPS also induced changes in differential gene expression in the muscle, MLN and blood at 4, 8, 24 and 48 hr post-injection. Engerix B and IFA elicited modest changes in the differential gene expression in the muscle at 48, 72 and 168 hr and at the later time points of 72 and 168 hr in the MLN, but very few differentially expressed genes were observed in the blood. Agrippal and Fluad exhibited similar patterns of differential gene expression at the later time points of 72 and 168 hr, but Fluad had an additional earlier signal at 8, 24 and 48 hr in the muscle (Figure 1). Figure 1 Download asset Open asset Overview of the transcriptomic responses to the injected vaccines and TLR agonists in the injected muscle, the draining lymph nodes (MLN), and the blood. The width of each violin is scaled to the number of genes that are differentially expressed at each given time point (BH-adjusted p-value <0.01). Differentially expressed genes were defined as those with a significant (BH-adjusted p-value <0.01) change in expression in a vaccinated group, when compared to the unvaccinated group and the saline group at a given time point. The upregulated or downregulated differentially expressed genes (Log2 fold-change) are presented for each tissue sampled and at each timepoint after injection of the vaccine or TLR agonist (n = 5 mice per timepoint). The vaccines or TLR agonists that were used are indicated on the right of the figure. IFA, incomplete Freunds adjuvant; LPS, lipopolysaccharide. https://doi.org/10.7554/eLife.46149.004 Distinctive transcriptional programs elicited after immunisation: gene-set enrichment and co-expression analysis Gene-set enrichment analysis was performed to identify the functional patterns of the biological processes that are induced by the different immunisations. Gene modules that are specific to the context of immune responses in blood tissue were previously defined by Li et al. (2014). These blood transcriptional modules were used to assess the signatures induced by the immunisations at the different time points. The blood transcriptional module enrichment profiles for every tissue are compared and presented in Figure 2 and Figure 2—figure supplements 1–3, where the gene sets with significant enrichment (lower than p<10−6) in each tissue are shown. In the injected muscle tissue, each vaccine or TLR agonist (with the exception of Agrippal) primarily elicited an upregulation of expression, as compared with the expresson levels seen after injection of the saline control, of genes related to inflammation, growth factors, innate immunity and cell damage (Figure 2—figure supplement 1). The induced transcriptomic response in the MLN was more refined, probably reflecting the effect of the migration of activated immune cells and local generalised stimulation by cyto/chemokine gradients. In these draining iliac lymph nodes, the injection of LPS, Pentavac SD and Poly I:C promoted an elevation of the interferon response genes as well as of cyto/chemokines (Figure 2—figure supplement 2). Finally, in the PBMCs, the transcriptomic analysis revealed that LPS, Pentavac SD and Poly I:C elicited an increase in gene transcripts that was associated with the interferon response and with activated monocytes and neutrophils (Figure 2—figure supplement 3). Figure 2 with 3 supplements see all Download asset Open asset Gene-set enrichment analysis reveals distinct mechanisms of transcriptional response to the injected vaccines and TLR agonists. Tissue data sets are presented as three coloured segments along the perimeter for the injected muscle (purple), the draining lymph nodes (green), and the blood compartment (red). An ordered list of blood transcriptional modules is labelled inside the outermost perimeter. Coloured bands adjacent to the module names correspond to the vaccines and Toll-like receptor (TLR) agonists (outside to inside bands): incomplete Freunds adjuvant (IFA) (grey), lipopolysaccharide (LPS) (blue), Pentavac SD (orange), Poly I:C (green), Fluad (red), Agrippal (purple), and Engerix (blue). For each band, there are six rings corresponding to time points 4, 8, 24, 48, 72, 168 hr (from outer to inner ring). The intensity of colours indicates the significance of the enrichment. Only modules with p-value for enrichment of <10−6 and an effect size (AUC) >0.8 are shown. DC, dendritic cell; ECM, extracellular matrix. https://doi.org/10.7554/eLife.46149.005 To identify transcriptional patterns in an unbiased way, gene modules were constructed using Weighted Gene Co-expression Network Analysis (WGCNA) (Langfelder and Horvath, 2008). Briefly, WGCNA discovers interacting genes by computing and matching the correlation patterns in their expression profiles over the entire time period, extracting clusters (modules) of highly correlated genes, and thereby categorizing the association between the modules and the traits (different vaccines). WGCNA was applied to the gene expression datasets for muscle, lymph nodes, and blood separately. The co-expression analyses identified 40 modules in muscle with numbers of genes ranging from 27 to 4979 (Figure 3—figure supplement 1A), 47 modules in lymph nodes with numbers of genes ranging from 26 to 3456 (Figure 3—figure supplement 1B), and 30 modules in blood with numbers of genes ranging from 24 to 9789 (Figure 3—figure supplement 1C). WGCNA assigns colours to name each module. We also added ‘mu’ for muscle, ‘ln’ for lymph nodes, or ‘bl’ for blood to each module name to avoid modules having the same name across different tissue datasets (such as muscle module (muM1_turquoise), lymph node module (lnM1_turquoise) and blood module (blM1_turquoise)). Figure 3—figure supplement 1 shows the correlations (r >0.3 positive correlations are in blue, r <−0.3 negative correlations are in red) between the modules and the immunisations. In muscle, co-expression analysis was able to identify modules that correlated with each immunisation, strongly (both positive and negative) for Pentavac SD and to a lesser degree for LPS, Fluad and Agrippal. These modules were different to those that correlated with a saline-only injection. In lymph nodes and blood, the method again revealed that Pentavac SD, LPS, Fluad and Agrippal treatments were associated with a number of positively and negatively significantly correlated modules. The connections between these highly correlated genes modules, which reveal whether the same sets of genes are co-regulated in the different tissues, are shown in Figure 3—figure supplement 1D, which identifies consensus modules associated with Agrippal, LPS and Pentavac SD. To interpret the extracted modules functionally, we compared the WGCNA modules with the reference blood transcriptional modules. A hypergeometric test was performed to quantify the overlap between the two sets of modules and the significant enrichments are reported in a heatmap (Figure 3). In muscle, the muM1-turquiose module contains genes that overlap with most of the reference modules and is associated positively with LPS and Pentavac SD; the muM2-blue module overlaps with reference modules of extracellular matrix (ECM), migration and mitochondria and is negatively associated with LPS and Pentavac SD (Figure 3 and Figure 3—figure supplement 1A). In lymph nodes, the lnM6-red module exhibits significant overlap with the reference modules associated with the cell cycle, whereas the lnM8-pink module is associated with interferon or antiviral sensing and this module contains genes that are co-regulated in LPS and Poly I:C immunisation (Figure 3 and Figure 3—figure supplement 1B). In blood, the blM1-turquoise module overlaps with reference modules of T cell, whereas the M4-yellow module overlaps with reference modules of monocytes, neutrophils and inflammatory/TLR/chemokines. The M1-turquoise and M4-yellow modules contain genes of similar transcriptional patterns that are significantly associated with LPS and Pentavac SD immunisation (Figure 3 and Figure 3—figure supplement 1C). Figure 3 with 1 supplement see all Download asset Open asset Heatmap representing the transcript overlap between WGCNA modules and the reference modules. Reference modules are the blood transcriptional modules defined by Li et al. (2014). These modules are shown in rows and are annotated within a higher functional group. Only WGCNA modules that showed a significant enrichment (hypergeometric test adjusted p-value<0.05) in any of the reference modules were included and shown in columns (hypergeometric test adjusted p-value<0.05). The legends on the figure report the strength of the p-value as a gradient of green. The colours at the top of the columnsindicate the tissue being analysed, whereas rows are colour-coded to indicate their higher annotation, as indicated in the key. https://doi.org/10.7554/eLife.46149.009 We developed an interactive web interface (available at https://vaccinebiomarkers.com) to facilitate data access and further discovery. This website allows users to (1) query genes and visualise their transcriptional profiles for each condition, (2) filter the differentially expressed genes by their functional groups and visualise the fold changes, and (3) analyse WGCNA modules by visualising the functional enrichments and listing the genes of each module. Identification of biomarkers that reflect potential vaccine inflammation In order to reveal potential connections between the different tissues, the differentially expressed genes and each sampling time point, we created a circular heatmap diagram showing genes that were common between the three tissues (Figure 4). We first selected the top 100 genes that were differentially regulated in both the injected muscle tissue and the MLN, then we assessed whether any of these genes were also differentially regulated in the circulating PBMC's RNA expression profiles. This analysis identified a set of genes that coded for soluble or cell-associated proteins present in each of the compartments that were differentially regulated by immunisation. We focused on soluble markers to simplify the sampling and quantification of potential blood biomarkers. The outside rim of the circular heatmap indicates the common individual genes, and the strength of the correlation for each tissue and each immunisation treatment is colour- and sized-coded. (Black indicates positive correlation, red indicates negative correlation; the thickness of the lines corresponds to the magnitude of the correlation coefficient.) Many of these genes encoded chemokines and/or cytokines but the analysis also identified proteins that collectively have been termed acute-phase proteins. These proteins are typically present at high levels during inflammatory events, and they include serum amyloid A-3 and 3 is an of the human and is in the response to in with and 2018; et al., whereas the by and dendritic cells et al., was strongly induced in muscle tissue after most of the immunisations exception being Agrippal, whereas in the draining lymph node and the LPS, Pentavac SD and I:C RNA expression LPS the in gene expression profiles of cyto/chemokine genes in the blood, and expression but not These differences were seen at the very early time points of 4 and hr and were not at 24 hr immunisation with the in the draining lymph the levels are not In the muscle, there is upregulation of and expression of also in the lymph node after LPS and Pentavac SD immunisation, whereas there is a generalised of in the The licensed vaccines, the Pentavac SD which the cell induced of the RNA expression levels in the Poly I:C strongly upregulated and and and in the blood, and this was different from the expression in the draining lymph node where and were and were (Figure 4). Figure 4 Download asset Open asset representing the differentially expressed genes and the observed correlations between each The three tissues analysed are as in Figure 2 and are indicated on the outer of the circular outer has a different to which injection they Fluad (red), Agrippal (purple), Poly I:C (green), Pentavac SD LPS (blue), IFA and Engerix B cell in the inner shows the fold change for each given time point, and tissue for upregulation and for with the of the corresponding to the magnitude of fold The lines the different of the correlation between tissues for each with lines showing a positive correlation and red lines a negative The thickness of the lines indicates the magnitude of the Identification of biomarkers in the serum We next examined whether a identified from the transcriptomics could be measured in the blood. We measured a of cyto/chemokines by and by in from mice vaccinated with saline control, the two licensed vaccines SD and and the two TLR agonists and Poly We selected these treatments on the of the of responses observed from the transcriptomic analysis. Pentavac SD, LPS and Poly I:C had signatures of responses in the MLN, Fluad but such responses were that these were candidates for cyto/chemokine in the peripheral blood 2 and 4). was collected from mice per and per analysis time point. LPS was inflammatory at early time points after expression of and treatment with LPS, and proteins were significantly and saline control levels at 4 and hr, with a to expression levels by 24 hr and to levels by hr (Figure of were significantly those in at 24 hr after LPS having a at 4 hr after treatment which then to by hr but was significantly and at by 24 hr immunisation. In polyI:C elicited levels of a number of these and at 4 hr – – and hr – – and and at 4 hr – – all treatments from the saline control elicited very high levels of expression of the which were at than those of any other measured and the kinetics of expression were also of a than those for expression of the cyto/chemokines (Figure The agonist LPS elicited the highest response of at 24 hr which by 48 hr a of by 72 hr and levels after 168 Although the Pentavac SD vaccination not the same of expression as the LPS the levels of to increase 48 hr with Pentavac SD and were 72 hr significantly levels at the analysis time point of 168 hr A of the total of after the LPS or Pentavac SD revealed that the for Pentavac SD was more than that of LPS, being and The agonist Poly I:C an expression profile in which the molecule at 4 hr to at hr and again to at 24 hr, these differences not significance when compared to the saline treatment with which contains the emulsion adjuvant not a in the expression of in the 24 hr that a of is induced by this Figure shows the fold changes over saline alone of measured proteins that are induced by different vaccines at the different time an of the degree and of expression over the In the of the LPS immunisation, this analysis showed that an of cyto/chemokines and can be defined to include and but not at 4 hr, then the same set of cyto/chemokines but at These of the differential transcriptomic expression in the blood with the levels of expressed proteins measurable in the animal revealed that of the proteins The expression of and proteins that was elicited by LPS immunisation was in with the measured transcriptomic changes at early time points and 24 By there is a in transcript levels for but protein levels are at 4, 8, and 24 The upregulation of following the Pentavac SD vaccination was in both transcript and protein Figure 5 with 1 supplement see all Download asset Open asset and chemokines and cytokines were measured using a and serum amyloid was measured by shows the expression of an individual in peripheral blood at 4, 8, 24, 48, 72 and 168 hr with the indicated vaccine (n = 5 mice per time levels induced by different vaccines that were in of those induced by saline alone were analysed using by representing the differential expression of all measured proteins in the induced by different vaccines shown as in the levels induced by saline alone at different time points. Data points corresponding to responses with adjusted p-value are labelled with the protein and coded to the time point. between transcripts and circulating cytokines We next the correlations between blood transcript and protein fold changes across all time points for all measured cyto/chemokines in the LPS immunisation group (Figure supplement 1).