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.

Sex-Biased Control of Inflammation and Metabolism by a Mitochondrial Nod-Like Receptor.

Mitochondria regulate steroid hormone synthesis, and in turn sex hormones regulate mitochondrial function for maintaining cellular homeostasis and controlling inflammation. This crosstalk can explain sex differences observed in several pathologies such as in metabolic or inflammatory disorders. Nod-like receptor X1 (NLRX1) is a mitochondria-associated innate receptor that could modulate metabolic functions and attenuates inflammatory responses. Here, we showed that in an infectious model with the human protozoan parasite, Leishmania guyanensis, NLRX1 attenuated inflammation in females but not in male mice. Analysis of infected female and male bone marrow derived macrophages showed both sex- and genotype-specific differences in both inflammatory and metabolic profiles with increased type I interferon production, mitochondrial respiration, and glycolytic rate in Nlrx1-deficient female BMDMs in comparison to wild-type cells, while no differences were observed between males. Transcriptomics of female and male BMDMs revealed an altered steroid hormone signaling in Nlrx1-deficient cells, and a "masculinization" of Nlrx1-deficient female BMDMs. Thus, our findings suggest that NLRX1 prevents uncontrolled inflammation and metabolism in females and therefore may contribute to the sex differences observed in infectious and inflammatory diseases.

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.

PamgeneAnalyzeR: open and reproducible pipeline for kinase profiling.

Protein phosphorylation--catalyzed by protein kinases-is the most common post-translational modification. It increases the functional diversity of the proteome and influences various aspects of normal physiology and can be altered in disease states. High throughput profiling of kinases is becoming an essential experimental approach to investigate their activity and this can be achieved using technologies such as PamChip® arrays provided by PamGene for kinase activity measurement. Here, we present 'pamgeneAnalyzeR', an R package developed as an alternative to the manual steps necessary to extract the data from PamChip® peptide microarrays images in a reproducible and robust manner. The extracted data can be directly used for downstream analysis. AVAILABILITY AND IMPLEMENTATION: PamgeneAnalyzeR is implemented in R and can be obtained from https://github.com/amelbek/pamgeneAnalyzeR.

Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers.

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists".

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation.

Database URL: http://biomodels.caltech.edu.