Emilio Siena

Shigellosis, an acute gastroenteritis infection caused by Shigella species, remains a public health burden in developing countries. Recently, many outbreaks due to Shigella sonnei multidrug-resistant strains have been reported in high-income countries, and the lack of an effective vaccine represents a major hurdle to counteract this bacterial pathogen. Vaccine candidates against Shigella sonnei are under clinical development, including a Generalized Modules for Membrane Antigens (GMMA)-based vaccine. The mechanisms by which GMMA-based vaccines interact and activate human immune cells remain elusive. Our previous study provided the first evidence that both adaptive and innate immune cells are targeted and functionally shaped by the GMMA-based vaccine. Here, flow cytometry and confocal microscopy analysis allowed us to identify monocytes as the main target population interacting with the S. sonnei 1790-GMMA vaccine on human peripheral blood. In addition, transcriptomic analysis of this cell population revealed a molecular signature induced by 1790-GMMA mostly correlated with the inflammatory response and cytokine-induced processes. This also impacts the expression of genes associated with macrophages’ differentiation and T cell regulation, suggesting a dual function for this vaccine platform both as an antigen carrier and as a regulator of immune cell activation and differentiation.

CD8+ T cells play a key role in mediating protective immunity after immune challenges such as infection or vaccination. Several subsets of differentiated CD8+ T cells have been identified, however, a deeper understanding of the molecular mechanism that underlies T-cell differentiation is lacking. Conventional approaches to the study of immune responses are typically limited to the analysis of bulk groups of cells that mask the cells’ heterogeneity (RNA-seq, microarray) and to the assessment of a relatively limited number of biomarkers that can be evaluated simultaneously at the population level (flow and mass cytometry). Single-cell analysis, on the other hand, represents a possible alternative that enables a deeper characterization of the underlying cellular heterogeneity. In this study, a murine model was used to characterize immunodominant hemagglutinin (HA_533-541)-specific CD8+ T-cell responses to nucleic- and protein-based influenza vaccine candidates, using single-cell sorting followed by transcriptomic analysis. Investigation of single-cell gene expression profiles enabled the discovery of unique subsets of CD8+ T cells that co-expressed cytotoxic genes after vaccination. Moreover, this method enabled the characterization of antigen specific CD8+ T cells that were previously undetected. Single-cell transcriptome profiling has the potential to allow for qualitative discrimination of cells, which could lead to novel insights on biological pathways involved in cellular responses. This approach could be further validated and allow for more informed decision making in preclinical and clinical settings.

Gene expression data is commonly used in vaccine studies to characterize differences between treatment groups or sampling time points. Group-wise comparisons of the transcriptional perturbations induced by vaccination have been applied extensively for investigating the mechanisms of action of vaccines. Such approaches, however, may not be sensitive enough for detecting changes occurring within a minority of the population under investigation or in single individuals. In this study, we developed a data analysis framework to characterize individual subject response profiles in the context of repeated measure experiments, which are typical of vaccine mode of action studies. Following the definition of the methodology, this was applied to the analysis of human transcriptome responses induced by vaccination with a subunit influenza vaccine. Results highlighted a substantial heterogeneity in how different subjects respond to vaccination. Moreover, the extent of transcriptional modulation experienced by each individual subject was found to be associated with the magnitude of vaccine-specific functional antibody response, pointing to a mechanistic link between genes involved in protein production and innate antiviral response. Overall, we propose that the improved characterization of the intersubject heterogeneity, enabled by our approach, can help driving the improvement and optimization of current and next-generation vaccines.

The current routine use of adjuvants in human vaccines provides a strong incentive to increase our understanding of how adjuvants differ in their ability to stimulate innate immunity and consequently enhance vaccine immunogenicity. Here, we evaluated gene expression profiles in cells from whole blood elicited in naive subjects receiving the hepatitis B surface antigen formulated with different adjuvants. We identified a core innate gene signature emerging 1 day after the second vaccination and that was shared by the recipients of vaccines formulated with adjuvant systems AS01_B, AS01_E, or AS03. This core signature associated with the magnitude of the hepatitis B surface-specific antibody response and was characterized by positive regulation of genes associated with interferon-related responses or the innate cell compartment and by negative regulation of natural killer cell–associated genes. Analysis at the individual subject level revealed that the higher immunogenicity of AS01_B-adjuvanted vaccine was linked to its ability to induce this signature in most vaccinees even after the first vaccination. Therefore, our data suggest that adjuvanticity is not strictly defined by the nature of the receptors or signaling pathways it activates but by the ability of the adjuvant to consistently induce a core inflammatory signature across individuals.

Neisseria meningitidis (Nm) is frequently found in the upper respiratory tract of the human population. Despite its prevalence as a commensal organism, Nm can occasionally invade the pharyngeal mucosal epithelium causing septicemia and life-threatening disease. A number of studies have tried to identify factors that are responsible for the onset of a virulent phenotype. Despite this however, we still miss clear causative elements. Several factors have been identified to be associated to an increased susceptibility to meningococcal disease in humans. None of them, however, could unambiguously discriminate healthy carrier from infected individuals. Similarly, comparative studies of virulent and apathogenic strains failed to identify virulence factors that could explain the emergence of the pathogenic phenotype. In line with this, a recent study of within host evolution found that Nm accumulates genomic changes during the asymptomatic carriage phase and that these are likely to contribute to the shift to a pathogenic phenotype. These results suggest that the presence of virulence factors in the meningococcal genome is not a sufficient condition for developing virulent traits, but is rather the ability to promote phenotypic variation, through the stochastic assortment of the repertoire of such factors, which could explain the occasional and unpredictable onset of IMD. Here, we present a series of argumentations supporting the hypothesis that invasive meningococcal disease comes as a result of the coexistence of bacterial virulence and variability factors in a plot that can be further complicated by additional latent factors, like host pre-existing immune status and genetic predisposition.

Systems biology has been recently applied to vaccinology to better understand immunological responses to the influenza vaccine. Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building from previous studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. Specifically, preexisting immunity against one or more vaccine antigens, which was found to negatively affect the seroresponse, was identified as a confounding factor able to decouple early transcriptome from later antibody responses, resulting in the degradation of a biomarker predictive power. Finally, the broadly accepted definition of seroresponse to influenza virus vaccine, represented by the maximum response across the vaccine-targeted strains, was compared to a composite measure integrating the responses against all strains. This analysis revealed that composite measures provide a more accurate assessment of the seroresponse to multicomponent influenza vaccines.

The Neisseria meningitidis (Nm) chromosome shows a high abundance of simple sequence DNA repeats (SSRs) that undergo stochastic, reversible mutations at high frequency. This mechanism is reflected in an extensive phenotypic diversity that facilitates Nm adaptation to dynamic environmental changes. To date, phase-variable phenotypes mediated by SSRs variation have been experimentally confirmed for 26 Nm genes. Here we present a population-scale comparative genomic analysis that identified 277 genes and classified them into 52 strong, 60 moderate and 165 weak candidates for phase variation. Deep-coverage DNA sequencing of single colonies grown overnight under non-selective conditions confirmed the presence of high-frequency, stochastic variation in 115 of them, providing circumstantial evidence for their phase variability. We confirmed previous observations of a predominance of variable SSRs within genes for components located on the cell surface or DNA metabolism. However, in addition we identified an unexpectedly broad spectrum of other metabolic functions, and most of the variable SSRs were predicted to induce phenotypic changes by modulating gene expression at a transcriptional level or by producing different protein isoforms rather than mediating on/off translational switching through frameshifts. Investigation of the evolutionary history of SSR contingency loci revealed that these loci were inherited from a Nm ancestor, evolved independently within Nm, or were acquired by Nm through lateral DNA exchange. Overall, our results have identified a broader and qualitatively different phenotypic diversification of SSRs-mediated stochastic variation than previously documented, including its impact on central Nm metabolism.