Dissection of the macrophage response towards infection by the Leishmania-viral endosymbiont duo and dynamics of the type I interferon response.

Leishmania RNA virus 1 (LRV1) is a double-stranded RNA virus found in some strains of the human protozoan parasite Leishmania, the causative agent of leishmaniasis, a neglected tropical disease. Interestingly, the presence of LRV1 inside Leishmania constitutes an important virulence factor that worsens the leishmaniasis outcome in a type I interferon (IFN)-dependent manner and contributes to treatment failure. Understanding how macrophages respond toward Leishmania alone or in combination with LRV1 as well as the role that type I IFNs may play during infection is fundamental to oversee new therapeutic strategies. To dissect the macrophage response toward infection, RNA sequencing was performed on murine wild-type and Ifnar-deficient bone marrow-derived macrophages infected with Leishmania guyanensis (Lgy) devoid or not of LRV1. Additionally, macrophages were treated with poly I:C (mimetic virus) or with type I IFNs. By implementing a weighted gene correlation network analysis, the groups of genes (modules) with similar expression patterns, for example, functionally related, coregulated, or the members of the same functional pathway, were identified. These modules followed patterns dependent on Leishmania, LRV1, or Leishmania exacerbated by the presence of LRV1. Not only the visualization of how individual genes were embedded to form modules but also how different modules were related to each other were observed. Thus, in the context of the observed hyperinflammatory phenotype associated to the presence of LRV1, it was noted that the biomarkers tumor-necrosis factor α (TNF-α) and the interleukin 6 (IL-6) belonged to different modules and that their regulating specific Src-family kinases were segregated oppositely. In addition, this network approach revealed the strong and sustained effect of LRV1 on the macrophage response and genes that had an early, late, or sustained impact during infection, uncovering the dynamics of the IFN response. Overall, this study contributed to shed light and dissect the intricate macrophage response toward infection by the Leishmania-LRV1 duo and revealed the crosstalk between modules made of coregulated genes and provided a new resource that can be further explored to study the impact of Leishmania on the macrophage response.

Predicting combinations of immunomodulators to enhance dendritic cell-based vaccination based on a hybrid experimental and computational platform.

Dendritic cell (DC)-based vaccines have been largely used in the adjuvant setting for the treatment of cancer, however, despite their proven safety, clinical outcomes still remain modest. In order to improve their efficacy, DC-based vaccines are often combined with one or multiple immunomodulatory agents. However, the selection of the most promising combinations is hampered by the plethora of agents available and the unknown interplay between these different agents. To address this point, we developed a hybrid experimental and computational platform to predict the effects and immunogenicity of dual combinations of stimuli once combined with DC vaccination, based on the experimental data of a variety of assays to monitor different aspects of the immune response after a single stimulus. To assess the stimuli behavior when used as single agents, we first developed an in vitro co-culture system of T cell priming using monocyte-derived DCs loaded with whole tumor lysate to prime autologous peripheral blood mononuclear cells in the presence of the chosen stimuli, as single adjuvants, and characterized the elicited response assessing 18 different phenotypic and functional traits important for an efficient anti-cancer response. We then developed and applied a prediction algorithm, generating a ranking for all possible dual combinations of the different single stimuli considered here. The ranking generated by the prediction tool was then validated with experimental data showing a strong correlation with the predicted scores, confirming that the top ranked conditions globally significantly outperformed the worst conditions. Thus, the method developed here constitutes an innovative tool for the selection of the best immunomodulatory agents to implement in future DC-based vaccines.