Platelet disturbances correlate with endothelial cell activation in uncomplicated Plasmodium vivax malaria.

Platelets drive endothelial cell activation in many diseases. However, if this occurs in Plasmodium vivax malaria is unclear. As platelets have been reported to be activated and to play a role in inflammatory response during malaria, we hypothesized that this would correlate with endothelial alterations during acute illness. We performed platelet flow cytometry of PAC-1 and P-selectin. We measured platelet markers (CXCL4, CD40L, P-selectin, Thrombopoietin, IL-11) and endothelial activation markers (ICAM-1, von Willebrand Factor and E-selectin) in plasma with a multiplex-based assay. The values of each mediator were used to generate heatmaps, K-means clustering and Principal Component analysis. In addition, we determined pair-wise Pearson's correlation coefficients to generate correlation networks. Platelet counts were reduced, and mean platelet volume increased in malaria patients. The activation of circulating platelets in flow cytometry did not differ between patients and controls. CD40L levels (Median [IQ]: 517 [406-651] vs. 1029 [732-1267] pg/mL, P = 0.0001) were significantly higher in patients, while P-selectin and CXCL4 showed a nonsignificant trend towards higher levels in patients. The network correlation approach demonstrated the correlation between markers of platelet and endothelial activation, and the heatmaps revealed a distinct pattern of activation in two subsets of P. vivax patients when compared to controls. Although absolute platelet activation was not strong in uncomplicated vivax malaria, markers of platelet activity and production were correlated with higher endothelial cell activation, especially in a specific subset of patients.

Methods for predicting vaccine immunogenicity and reactogenicity.

Subjects receiving the same vaccine often show different levels of immune responses and some may even present adverse side effects to the vaccine. Systems vaccinology can combine omics data and machine learning techniques to obtain highly predictive signatures of vaccine immunogenicity and reactogenicity. Currently, several machine learning methods are already available to researchers with no background in bioinformatics. Here we described the four main steps to discover markers of vaccine immunogenicity and reactogenicity: (1) Preparing the data; (2) Selecting the vaccinees and relevant genes; (3) Choosing the algorithm; (4) Blind testing your model. With the increasing number of Systems Vaccinology datasets being generated, we expect that the accuracy and robustness of signatures of vaccine reactogenicity and immunogenicity will significantly improve.

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination.

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of systems vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes, but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to influenza and yellow fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B cells and correlates with the expression of immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-sequencing (RNA-seq) data from a cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide evidence that lncRNAs have a significant impact on immune responses induced by vaccination.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. RESULTS: In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. CONCLUSION: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.