Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses

The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets

Background

Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally.

Methods

Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information.

Results

We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity.

Conclusions

We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses.

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Artificial Intelligence Applied to in vitro Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children

The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from in-vitro stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.

NK Cell Memory to Cytomegalovirus: Implications for Vaccine Development

Natural killer (NK) cells are innate lymphoid cells that recognize and eliminate virally-infected and cancerous cells. Members of the innate immune system are not usually considered to mediate immune memory, but over the past decade evidence has emerged that NK cells can do this in several contexts. Of these, the best understood and most widely accepted is the response to cytomegaloviruses, with strong evidence for memory to murine cytomegalovirus (MCMV) and several lines of evidence suggesting that the same is likely to be true of human cytomegalovirus (HCMV). The importance of NK cells in the context of HCMV infection is underscored by the armory of NK immune evasion genes encoded by HCMV aimed at subverting the NK cell immune response. As such, ongoing studies that have utilized HCMV to investigate NK cell diversity and function have proven instructive. Here, we discuss our current understanding of NK cell memory to viral infection with a focus on the response to cytomegaloviruses. We will then discuss the implications that this will have for the development of a vaccine against HCMV with particular emphasis on how a strategy that can harness the innate immune system and NK cells could be crucial for the development of a vaccine against this high-priority pathogen.

Comparison of Dendritic Cell Activation by Virus-Based Vaccine Delivery Vectors Emphasizes the Transcriptional Downregulation of the Oxidative Phosphorylation Pathway

Antigen delivery platforms based on engineered viruses or virus-like particles are currently developed as vaccines against infectious diseases. As the interaction of vaccines with dendritic cells (DCs) shapes the immunological response, we compared the interaction of a range of virus-based vectors and virus-like particles with DCs in a murine model of systemic administration and transcriptome analyses of splenic DCs. The transcriptome profiles of DCs separated the vaccine vectors into two distinct groups characterized by high- and low-magnitude differential gene expression, which strongly correlated with (1) the surface expression of costimulatory molecules CD40, CD83, and CD86 on DCs, and (2) antigen-specific T-cell responses. Pathway analysis using PANOGA (Pathway and Network-Oriented GWAS Analysis) revealed that the JAK/STAT pathway was significantly activated by both groups of vaccines. In contrast, the oxidative phosphorylation pathway was significantly downregulated only by the high-magnitude DC-stimulating vectors. A gene signature including exclusively chemokine-, cytokine-, and receptor-related genes revealed a vector-specific pattern. Overall, this in vivo DC stimulation model demonstrated a strong relationship between the levels of induced DC maturation and the intensity of T-cell-specific immune responses with a distinct cytokine/chemokine profile, metabolic shifting, and cell surface expression of maturation markers. It could represent an important tool for vaccine design.

Transcriptome profiling in blood before and after hepatitis B vaccination shows significant differences in gene expression between responders and non-responders

INTRODUCTION

As the hepatitis B virus is widely spread and responsible for considerable morbidity and mortality, WHO recommends vaccination from infancy to reduce acute infection and chronic carriers. However, current subunit vaccines are not 100% efficacious and leave 5-10% of recipients unprotected.

METHODS

To evaluate immune responses after Engerix-B vaccination, we determined, using mRNA-sequencing, whole blood early gene expression signatures before, at day 3 and day 7 after the first dose and correlated this with the resulting antibody titer after two vaccine doses.

RESULTS

Our results indicate that immune related genes are differentially expressed in responders mostly at day 3 and in non-responders mostly at day 7. The most remarkable difference between responders and non-responders were the differentially expressed genes before vaccination. The granulin precursor gene (GRN) was significantly downregulated in responders while upregulated in non-responders at day 0. Furthermore, absolute granulocytes numbers were significantly higher in non-responders at day 0.

CONCLUSION

The non-responders already showed an activated state of the immune system before vaccination. Furthermore, after vaccination, they exhibited a delayed and partial immune response in comparison to the responders. Our data may indicate that the baseline and untriggered immune system can influence the response upon hepatitis B vaccination.